Methods, systems, and compositions for classification, prognosis, and diagnosis of cancers

ABSTRACT

The present invention provides methods, systems and compositions for predicting disease susceptibility in a patient. In some embodiments, methods for the classification, prognosis, and diagnosis of cancers are provided. In other embodiments, the present invention provides statistical methods for building a gene-expression-based classifier that may be employed for predicting disease susceptibility in a patient, for classifying carcinomas, and for the prognosis of clinical outcomes.

FIELD OF THE INVENTION

The present invention relates generally to systems, compositions, and methods for predicting disease susceptibility in a patient.

BACKGROUND

Mutations in p53 are thought to occur in more than 50% of human cancers and are most frequently observed in the DNA binding and transactivation domains, underscoring the importance of its transcriptional activity in suppressing tumor development. In sporadic breast cancers, unlike most cancer types, p53 mutations are only observed in approximately 20% of cases. However, that breast cancer is frequently observed in individuals with germline mutations of p53 (i.e., Li-Fraumeni syndrome) suggests a particularly important role for p53 inactivation in breast carcinogenesis, and perhaps a similarly important role for other factors capable of compromising p53 function.

For example, the reduced transcriptional activation of p53 following hypermethylation and subsequent inhibition of the HOXA5 transcription factor has recently been implicated as a possible epigenetic mechanism in reducing p53 expression in breast cancers. In both breast tumors and other cancer types, amplification and overexpression of the MDM2 gene, whose product promotes p53 degradation, has been implicated in oncogenesis. Moreover, both deletion and epigenetic silencing of the p14ARF gene, a negative regulator of MDM2, has been observed in various cancer types. Thus, p53 deficiency in breast carcinogenesis can potentially arise from a number of mechanisms other than p53 gene mutation.

There is evidence that the p53 status has prognostic significance in a number of cancer types and in particular breast cancer. In breast cancer, p53 mutations confer worse overall and disease-free survival, and a higher incidence of tumor recurrence, independent of other risk factors. Recent evidence suggests that p53 inactivation renders breast tumors resistant to certain DNA-damaging chemotherapies and endocrine therapies presumably through loss of p53-dependent apoptosis.

However, in all of these studies, the prognostic capability and degree of therapeutic resistance of the p53 mutants was found to depend largely on mutant-specific attributes, such as the type of mutations or the precise domain in which the mutation occurs. Importantly, this latter observation is consistent with findings from previous studies showing that not all p53 mutations have equal effects: some simply confer loss of function, while others have a dominant negative effect (such as trans-dominant suppression of wildtype p53 or oncogenic gain of function), while still others show only a partial loss of function where, for example, only a small subset of p53 downstream transcriptional target genes are dysregulated. For these reasons, no single molecular assessment of p53 status appears to provide an absolute indication of the complete p53 function.

There is a need for methods that better assess the effects of different p53 mutations on cell function in general and gene expression in particular, in an effort to enable better cancer prognosis and diagnosis.

SUMMARY

Accordingly, the present invention provides methods, systems, and compositions that provide a more useful measure of in vivo p53 functionality. These methods, systems, and compositions may be employed for the classification, prognosis, and diagnosis of cancers.

In one aspect of the present invention there is provided a method for predicting disease outcome in a patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the patient.

In another aspect of the present invention there is provided a method for predicting disease outcome in a late-stage breast cancer patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the late-stage breast cancer patient wherein the set of genes are selected from the group consisting of GenBank accession numbers: BG271923 (SEQ ID NO: 22), NM_(—)002466 (SEQ ID NO: 31), D38553 (SEQ ID NO: 11), NM_(—)000909 (SEQ ID NO: 9), NM_(—)024843 (SEQ ID NO: 1), R73030 (SEQ ID NO: 29), NM_(—)003226 (SEQ ID NO: 28), AW299538 (SEQ ID NO: 5) and AI990465 (SEQ ID NO: 25).

In yet another aspect of the present invention there is provided a method for predicting clinical outcome in an early-stage, locally-treated breast cancer patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the early-stage, locally-treated breast cancer patient wherein the set of genes are selected from the group consisting of GenBank accession numbers: AI961235 (SEQ ID NO 23), BG271923 (SEQ ID NO: 22), NM_(—)002466 (SEQ ID NO: 31), BC001651 (SEQ ID NO: 14), D38553 (SEQ ID NO: 11), AK000345 (SEQ ID NO: 26), BC004504 (SEQ ID NO: 8), NM_(—)000909 (SEQ ID NO: 9), NM_(—)024843 (SEQ ID NO: 1), R73030 (SEQ ID NO: 29), AI435828 (SEQ ID NO: 20), AI810764 (SEQ ID NO: 24), AI922323 (SEQ ID NO: 10), NM_(—)003225 (SEQ ID NO: 32), NM_(—)003226 (SEQ ID NO: 28), AW299538 (SEQ ID NO: 5), NM_(—)003462 (SEQ ID NO: 16), AI990465 (SEQ ID NO: 25), NM_(—)004392 (SEQ ID NO: 15), NM_(—)001267 (SEQ ID NO: 7) and AI826437 (SEQ ID NO: 3).

In a further aspect of the present invention there is provided a method for predicting clinical outcome in a liver cancer patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the liver cancer patient wherein the set of genes are selected from the group consisting of GenBank accession numbers: NM_(—)002466 (SEQ ID NO: 31), BC001651 (SEQ ID NO: 14), D38553 (SEQ ID NO: 11), NM_(—)024843 (SEQ ID NO: 1), AI435828 (SEQ ID NO: 20), AI810764 (SEQ ID NO: 24), NM_(—)003226 (SEQ ID NO: 28) and AW299538 (SEQ ID NO: 5).

In a still further aspect of the present invention there is provided a method of identifying a group of genes for predicting disease outcome in a patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; ranking the differentially expressed genes according to their ability to predict p53 mutational status; training the ranked genes to distinguish between mutant and wildtype p53 gene expression profiles; obtaining a p53 classifier including a set of genes capable of predicting p53 mutational status; validating the p53 classifier in independent datasets; and assessing the ability of the p53 classifier to predict disease outcome in the patient.

In another aspect of the present invention there is provided a computer system for predicting disease outcome in a patient, the computer system comprising: a computer having a processor and a memory, the memory having executable code stored thereon for execution by the processor for performing the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the patient.

In yet another aspect of the present invention there is provided a diagnostic tool for predicting disease susceptibility in a patient comprising a plurality of genes capable of predicting p53 mutational status immobilized on a solid support.

In a still further aspect of the present invention there is provided a nucleic acid array for predicting disease susceptibility in a patient comprising a solid support and displayed thereon nucleic acid probes corresponding to genes capable of predicting p53 mutational status in the patient.

These aspects and embodiments are described in greater detail below.

Definitions

As used in this application, the singular form “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “an agent” includes a plurality of agents, including mixtures thereof.

An individual is not limited to a human being but may also be other organisms including but not limited to a mammal, invertebrate, plant, fungus, virus, bacteria, or one or more cells derived from any of the above.

As used herein the term “comprising” means “including”. Variations of the word “comprising”, such as “comprise” and “comprises”, have correspondingly varied meanings.

Throughout this disclosure, various aspects of this invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

As used herein, the term “histologic grade” or “tumor grade” refers to characteristics of tumors classified according to the Elston-Ellis system of grading tumors.

As used herein, “p53 status” refers to the mutational status of the p53 gene. A p53 mutant tumor contains a mutation in the p53 gene that alters the function of the protein. A p53 wildtype tumor contains no detectable mutation in the p53 gene.

As used herein “Disease-specific survival” or DSS is a survival assessment where the end point being examined is death because of a disease, for example, breast cancer.

As used herein, “Disease-free survival” or DFS is a survival assessment where the end points are either tumor recurrence (i.e., the cancer comes back as the consequence of distant metastasis to other sites in the body) or death because of breast cancer without evidence of distant metastasis.

As used herein, an “array” is an intentionally created collection of molecules which can be prepared either synthetically or biosynthetically. The molecules in the array can be identical or different from each other. The array can assume a variety of formats, e.g., libraries of soluble molecules; libraries of compounds tethered to resin beads, silica chips, or other solid supports.

As used herein, a “nucleic acid library or array” is an intentionally created collection of nucleic acids which can be prepared either synthetically or biosynthetically in a variety of different formats (e.g., libraries of soluble molecules; and libraries of oligonucleotides tethered to resin beads, silica chips, or other solid supports). Additionally, the term “array” is meant to include those libraries of nucleic acids which can be prepared by spotting nucleic acids of essentially any length (e.g., from 1 to about 1000 nucleotide monomers in length) onto a substrate. The term “nucleic acid” as used herein refers to a polymeric form of nucleotides of any length, either ribonucleotides, deoxyribonucleotides or peptide nucleic acids (PNAs), that comprise purine and pyrimidine bases, orpther natural, chemically or biochemically modified, non-natural, or derivatized nucleotide bases. The backbone of the polynucleotide can comprise sugars and phosphate groups, as may typically be found in RNA or DNA, or modified or substituted sugar or phosphate groups. A polynucleotide may comprise modified nucleotides, such as methylated nucleotides and nucleotide analogs. The sequence of nucleotides may be interrupted by non-nucleotide components. Thus the terms nucleoside, nucleotide, deoxynucleoside and deoxynucleotide generally include analogs such as those described herein. These analogs are those molecules having some structural features in common with a naturally occurring nucleoside or nucleotide such that when incorporated into a nucleic acid or oligonucleotide sequence, they allow hybridization with a naturally occurring nucleic acid sequence in solution. Typically, these analogs are derived from naturally occurring nucleosides and nucleotides by replacing and/or modifying the base, the ribose or the phosphodiester moiety. The changes can be tailor made to stabilize or destabilize hybrid formation or enhance the specificity of hybridization with a complementary nucleic acid sequence as desired.

As used herein, the term “complementary” refers to the hybridization or base pairing between nucleotides or nucleic acids, such as, for instance, between the two strands of a double stranded DNA molecule or between an oligonucleotide primer and a primer binding site on a single stranded nucleic acid to be sequenced or amplified. Complementary nucleotides are, generally, A and T (or A and U), or C and G. Two single stranded RNA or DNA molecules are said to be complementary when the nucleotides of one strand, optimally aligned and compared and with appropriate nucleotide insertions or deletions, pair with at least about 80% of the nucleotides of the other strand, usually at least about 90% to 95%, and more preferably from about 98 to 100% of the nucleotides of the other strand. Alternatively, complementarity exists when an RNA or DNA strand will hybridize under selective hybridization conditions to its complement. Typically, selective hybridization will occur when there is at least about 65% complementarity over a stretch of at least 14 to 25 nucleotides, preferably at least about 75%, and more preferably at least about 90% complementarity.

As used herein, a “fragment,” “segment,” or “DNA segment” refers to a portion of a larger DNA polynucleotide or DNA. A polynucleotide, for example, can be broken up, or fragmented into, a plurality of segments. Various methods of fragmenting nucleic acids are well known in the art. These methods may be, for example, either chemical or physical in nature. Chemical fragmentation may include partial degradation with a DNase; partial depurination with acid; the use of restriction enzymes; intron-encoded endonucleases; DNA-based cleavage methods, such as triplex and hybrid formation methods, that rely on the specific hybridization of a nucleic acid segment to localize a cleavage agent to a specific location in the nucleic acid molecule; or other enzymes or compounds which cleave DNA at known or unknown locations. Physical fragmentation methods may involve subjecting the DNA to a high shear rate. High shear rates may be produced, for example, by moving DNA through a chamber or channel with pits or spikes, or forcing the DNA sample through a restricted size flow passage, e.g., an aperture having a cross sectional dimension in the micron or submicron scale. Other physical methods include sonication and nebulization. Combinations of physical and chemical fragmentation methods may likewise be employed such as fragmentation by heat and ion-mediated hydrolysis. See for example, Sambrook et al., “Molecular Cloning: A Laboratory Manual,” 3rd Ed. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (2001) (“Sambrook et al.) which is incorporated herein by reference for all purposes. These methods can be optimized to digest a nucleic acid into fragments of a selected size range. Useful size ranges may be from 100, 200, 400, 700 or 1000 to 500, 800, 1500, 2000, 4000 or 10,000 base pairs. However, larger size ranges such as 4000, 10,000 or 20,000 to 10,000, 20,000 or 500,000 base pairs may also be useful.

As used herein, the term “hybridization” refers to the process in which two single-stranded polynucleotides bind non-covalently to form a stable double-stranded polynucleotide. The term “hybridization” may also refer to triple-stranded hybridization. The resulting (usually) double-stranded polynucleotide is a “hybrid.” The proportion of the population of polynucleotides that forms stable hybrids is referred to herein as the “degree of hybridization”. Hybridization conditions will typically include salt concentrations of less than about 1M, more usually less than about 500 mM and less than about 200 mM. Hybridization temperatures can be as low as 5° C., but are typically greater than 22° C., more typically greater than about 30° C., and preferably in excess of about 37° C. Hybridizations are usually performed under stringent conditions, i.e. conditions under which a probe will hybridize to its target subsequence. Stringent conditions are sequence-dependent and are different in different circumstances. Longer fragments may require higher hybridization temperatures for specific hybridization. As other factors may affect the stringency of hybridization, including base composition and length of the complementary strands, presence of organic solvents and extent of base mismatching, the combination of parameters is more important than the absolute measure of any one alone. Generally, stringent conditions are selected to be about 5° C. lower than the thermal melting point (T_(m)) for the specific sequence at a defined ionic strength and pH. The T_(m), is the temperature (under defined ionic strength, pH and nucleic acid composition) at which 50% of the probes complementary to the target sequence hybridize to the target sequence at equilibrium.

Typically, stringent conditions include salt concentration of at least 0.01 M to no more than 1 M Na ion concentration (or other salts) at a pH 7.0 to 8.3 and a temperature of at least 25° C. For example, conditions of 5× SSPE (750 mM NaCl, 50 mM NaPhosphate, 5 mM EDTA, pH 7.4) and a temperature of 25-30° C. are suitable for allele-specific probe hybridizations. For stringent conditions, see for example, Sambrook, Fritsche and Maniatis. “Molecular Cloning A Laboratory Manual” 2nd Ed. Cold Spring Harbor Press (1989) and Anderson “Nucleic Acid Hybridization” 1st Ed., BIOS Scientific Publishers Limited (1999), which are hereby incorporated by reference in their entireties for all purposes above.

As used herein, “hybridization probes” are nucleic acids (such as oligonucleotides) capable of binding in a base-specific manner to a complementary strand of nucleic acid. Such probes include peptide nucleic acids, as described in Nielsen et al., Science 254:1497-1500 (1991), Nielsen Curr. Opin. Biotechnol., 10:71-75 (1999) and other nucleic acid analogs and nucleic acid mimetics.

As used herein, “mRNA” or “mRNA transcripts” include, but are not limited to pre-mRNA transcript(s), transcript processing intermediates, mature mRNA(s) ready for translation and transcripts of the gene or genes, or nucleic acids derived from the mRNA transcript(s). Transcript processing may include splicing, editing and degradation. As used herein, a nucleic acid derived from an mRNA transcript refers to a nucleic acid for whose synthesis the mRNA transcript or a subsequence thereof has ultimately served as a template. Thus, a cDNA reverse transcribed from an mRNA, a cRNA transcribed from that cDNA, a DNA amplified from the cDNA, an RNA transcribed from the amplified DNA, etc., are all derived from the mRNA transcript and detection of such derived products is indicative of the presence and/or abundance of the original transcript in a sample. Thus, mRNA derived samples include, but are not limited to, mRNA transcripts of the gene or genes, cDNA reverse transcribed from the mRNA, cRNA transcribed from the cDNA, DNA amplified from the genes, RNA transcribed from amplified DNA, and the like.

As used herein, a “probe” is a molecule that can be recognized by a particular target. In some embodiments, a probe can be surface immobilized. Examples of probes that can be investigated by this invention include, but are not restricted to, agonists and antagonists for cell membrane receptors, toxins and venoms, viral epitopes, hormones (e.g. opioid peptides, steroids, etc.), hormone receptors, peptides, enzymes, enzyme substrates, cofactors, drugs, lectins, sugars, oligonucleotides, nucleic acids, oligosaccharides, proteins, and monoclonal antibodies.

As used herein, a “target” is a molecule that has an affinity for a given probe. Targets may be naturally-occurring or man-made molecules. Also, they can be employed in their unaltered state or as aggregates with other species. Targets may be attached, covalently or noncovalently, to a binding member, either directly or via a specific binding substance. Examples of targets which can be employed by this invention include, but are not restricted to, antibodies, cell membrane receptors, monoclonal antibodies and antisera reactive with specific antigenic determinants (such as on viruses, cells or other materials), drugs, oligonucleotides, nucleic acids, peptides, cofactors, lectins, sugars, polysaccharides, cells, cellular membranes, and organelles. Targets are sometimes referred to in the art as anti-probes. As the term targets is used herein, no difference in meaning is intended. A “Probe Target Pair” is formed when two macromolecules have combined through molecular recognition to form a complex.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copes of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the disclosed principles of the invention:

FIG. 1 shows hierarchical clustering of 257 tumors using the top 250 genes statistically correlated with p53 status for use in one disclosed embodiment of the invention.

FIG. 2 shows optimization and results of a gene classifier for p53 status in accordance with a disclosed embodiment of the invention.

FIG. 3 shows that genes of the classifier can predict p53 status in independent cDNA microarray datasets in accordance with a disclosed embodiment of the invention.

FIG. 4 shows that the p53 classifier has greater prognostic significance than p53 mutation status alone in accordance with a disclosed embodiment of the invention.

FIG. 5 shows that the p53 classifier has strong prognostic significance in an independent dataset of late-stage tumors in accordance with a disclosed embodiment of the invention.

FIG. 6 shows that the p53 classifier has greater prognostic significance than p53 mutation status in endocrine-treated patients in accordance with a disclosed embodiment of the invention.

FIG. 7 shows that the p53 classifier is prognostic of distant recurrence in an independent set of early-stage locally-treated breast tumors in accordance with a disclosed embodiment of the invention.

FIG. 8 shows that transcript levels of p53, its transcriptional targets, and its upstream effectors distinguish known and predicted classes in accordance with a disclosed embodiment of the invention.

DETAILED DESCRIPTION

The present invention has many preferred embodiments and relies on many patents, applications and other references for details known to those of the art. Therefore, when a patent, application, or other reference is cited or repeated below, it should be understood that it is incorporated by reference in its entirety for all purposes as well as for the proposition that is recited.

Embodiments of the disclosed methods, systems, and compositions for classification, prognosis, and diagnosis of cancers will now be described. These methods, systems, and compositions provide a more useful measure of in vivo p53 functionality and thereby provide a better prognostic indicator of patient outcome as compared to p53 mutation status alone. Other advantages inherent in the disclosed embodiments of the methods, systems, and compositions will be apparent from the following description.

p53 mutations in cancer development and progression can result in trans-dominant suppression of the wild-type p53 allele conferring loss of p53 activity or an oncogenic gain of function independent of wildtype p53. Additionally, the altered activity of some effectors of p53 function, including those that directly influence p53 expression, may contribute to p53 deficiency recapitulating the p53-mutant phenotype. In breast cancer, these effects manifest in more aggressive tumors, therapeutic resistance, and poor clinical outcome.

In accordance with providing a more useful measure of in vivo p53 functionality, disclosed herein is a “p53 classifier”, an expression signature deduced from differences in the molecular configurations of p53 wildtype and mutant tumors. The classifier may comprise a defined number of genes, for example, at least 3 genes. In other embodiments, the classifier may comprise from about 3 genes to about 500 genes. Table 1 provides a listing of the 500 genes. In some embodiments, an optimized p53 classifier comprises 32 genes (Table 2). The optimized 32-gene classifier could distinguish p53 mutant and wildtype tumors with significant accuracy and could predict recurrence and survival in populations representing all therapeutic groups. Moreover, the p53 classifier was a more significant predictor of survival than p53 mutation status alone and remained significant by multivariate analysis independent of other clinical predictors where p53 mutation status did not. Furthermore, downregulation of p53 expression in the absence of mutations was sufficient to induce a mutant (mt) phenotype tumor behaviour in both transcriptional activity and clinical outcome.

In independent datasets of both breast and liver cancers, and regardless of other clinical features, subsets of the optimized p53 classifier could predict p53 status with significant accuracy. As a predictor of disease-specific survival (DSS), the classifier significantly outperformed p53 mutational status alone in both a large patient cohort with heterogeneous treatment, as well as in a set of patients who received postoperative adjuvant endocrine therapy alone.

Moreover, in an independent cDNA microarray study comprised mostly of stage 3 patients who received chemotherapy in the neoadjuvant setting, a 9-gene subset of the p53 classifier was a highly significant predictor of both disease-specific and disease-free survival. The genes of the p53 classifier could accurately discern not only which patients would relapse and die following chemotherapy, but also which late stage patients would survive their cancer.

A 21-gene subset of the classifier could also significantly distinguish molecular subgroups of early-stage radiation-treated patients who would go on to develop a distant metastasis within 5 years from those who would not.

Therefore, by defining among other aspects, a p53 classifier described herein, the methods, systems and compositions of the present invention demonstrate a much greater impact of p53 on human tumor behaviour than previously appreciated and thereby provide a better approach for clinically assessing p53 function.

One aspect of the present invention provides a method for predicting disease outcome in a patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the patient. The disease outcome may be selected from the group consisting of disease-specific survival, disease-free survival, tumor recurrence and therapeutic response. The disease may be any cancer but is preferably breast cancer or liver cancer.

The predicted p53 mutational status may be obtained by ranking the differentially expressed genes according to their association with p53 mutational status, ER (estrogen receptor) status and histologic grade of the tumor. A multivariate ranking procedure such as a Linear Model Fit may be employed to rank the genes. The ranked genes may be subjected to supervised learning to enable them to distinguish between mutant and wildtype gene expression profiles. An example of a supervised learning method that may be employed is Diagonal Linear Discriminant Analysis (DLDA).

In some embodiments, the set of genes with the ability to predict p53 mutational status may comprise at least 3 genes, preferably about 3-500 genes and most preferably about 32 genes. The 32 genes making up the optimized p53 classifier may be selected from the group comprising the list of genes in Table 1. In some embodiments, the 32 genes may include GenBank accession numbers: AI961235 (SEQ ID NO: 23), BG271923 (SEQ ID NO: 22), NM_(—)002466 (SEQ ID NO: 31), BC001651 (SEQ ID NO: 14), D38553 (SEQ ID NO: 11), AK000345 (SEQ ID NO: 26), AA742697 (SEQ ID NO: 21), AL080170 (SEQ ID NO: 30), BF245284 (SEQ ID NO: 27), BC004504 (SEQ ID NO: 8), H15261 (SEQ ID NO: 2), NM_(—)000909 (SEQ ID NO: 9), NM_(—)024843 (SEQ ID NO: 1), R73030 (SEQ ID NO: 29), NM_(—)030896 (SEQ ID NO: 17), AI435828 (SEQ ID NO: 20), AL512727 (SEQ ID NO: 6), AW242997 (SEQ ID NO: 18), AI810764 (SEQ ID NO: 24), AI922323 (SEQ ID NO: 10), AL360204 (SEQ ID NO: 13), NM_(—)003225 (SEQ ID NO: 32), NM_(—)003226 (SEQ ID NO: 28), AW299538 (SEQ ID NO: 5), NM_(—)003462 (SEQ ID NO: 16), AI990465 (SEQ ID NO: 25), NM_(—)004392 (SEQ ID NO: 15), NM_(—)001267 (SEQ ID NO: 7), AF269087 (SEQ ID NO: 4), AI826437 (SEQ ID NO: 3), AL355392 (SEQ ID NO: 12), and AU156421 (SEQ ID NO: 19).

The present invention also provides a method for predicting disease outcome in a late-stage breast cancer patient, the method comprising the steps of obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the late-stage breast cancer patient wherein the set of genes are selected from the group consisting of GenBank accession numbers: BG271923, NM_(—)002466, D38553, NM_(—)000909, NM_(—)024843, R73030, NM_(—)003226, AW299538 and AI990465. All GenBank accession numbers are associated with a sequence and a SEQ ID NO. as shown in FIGS. 9-508.

The present invention also provides a method for predicting clinical outcome in an early-stage, locally-treated breast cancer patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the early-stage, locally-treated breast cancer patient wherein the set of genes are selected from the group consisting of GenBank accession numbers: AI961235, BG271923, NM_(—)002466, BC001651, D38553, AK000345, BC004504, NM_(—)000909, NM_(—)024843, R73030, AI435828, AI810764, AI922323, NM_(—)003225, NM_(—)003226, AW299538, NM_(—)003462, AI990465, NM_(—)004392, NM_(—)001267 and AI826437.

The present invention also provides a method for predicting clinical outcome in a liver cancer patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the liver cancer patient wherein the set of genes are selected from the group consisting of GenBank accession numbers: NM_(—)002466, BC001651, D38553, NM_(—)024843, AI435828, AI810764, NM_(—)003226 and AW299538.

The present invention also provides a method of identifying a group of genes for predicting disease outcome in a patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; ranking the differentially expressed genes according to their ability to predict p53 mutational status; training the ranked genes to distinguish between mutant and wildtype p53 gene expression profiles; obtaining a p53 classifier including a set of genes capable of predicting p53 mutational status; validating the p53 classifier in independent datasets; and assessing the ability of the p53 classifier to predict disease outcome in the patient.

In the above-disclosed method of identifying a group of genes for predicting disease outcome in a patient, the differentially expressed genes may be ranked by a multivariate ranking procedure according to their association with p53 status, ER (estrogen receptor) status and histologic grade of the tumor. The multivariate ranking procedure may be a Linear Model-Fit method or any other method known to one of skill in the art. The step of training may comprise employing a supervised learning method, such as Diagonal Linear Discriminant Analysis (DLDA) or any other supervised learning method known to one of skill in the art.

The p53 classifier disclosed above may comprise at least 3 genes, preferably between about 3-500 genes and more preferably about 32 genes. This 32-gene p53 classifier is an “optimized classifier” which may include genes selected from the group consisting of GenBank accession numbers: AI961235, BG271923, NM_(—)002466, BC001651, D38553, AK000345, AA742697, AL080170, BF245284, BC004504, H15261, NM_(—)000909, NM_(—)024843, R73030, NM_(—)030896, AI435828, AL512727, AW242997, AI810764, A1922323, AL360204, NM_(—)003225, NM_(—)003226, AW299538, NM_(—)003462, AI990465, NM_(—)004392, NM_(—)001267, AF269087, AI826437, AL355392 and AU156421.

The disease outcome may be selected from the group consisting of disease-specific survival, disease-free survival, tumor recurrence and therapeutic response. In one disclosed embodiment, a 9-gene partial classifier may predict clinical outcome in a late-stage breast cancer patient. The 9-gene partial classifier may include genes selected from the group consisting of GenBank accession numbers: BG271923, NM_(—)002466, D38553, NM_(—)000909, NM_(—)024843, R73030, NM_(—)003226, AW299538 and AI990465.

In another disclosed embodiment, a 21-gene partial classifier may predict clinical outcome in an early-stage, locally-treated breast cancer patient. The 21-gene partial classifier may include genes selected from the group consisting of GenBank accession numbers: AI961235, BG271923, NM_(—)002466, BC001651, D38553, AK000345, BC004504, NM_(—)000909, NM_(—)024843, R73030, AI435828, AI810764, AI922323, NM_(—)003225, NM_(—)003226, AW299538, NM_(—)003462, AI990465, NM_(—)004392, NM_(—)001267 and AI826437.

In yet another disclosed embodiment, a 8-gene partial classifier may predict clinical outcome in a liver cancer patient. The 8-gene partial classifier may include genes selected from the group consisting of GenBank accession numbers: NM_(—)002466, BC001651, D38553, NM_(—)024843, AI435828, AI810764, NM_(—)003226 and AW299538.

The present invention also provides a computer system for predicting disease outcome in a patient, the computer system comprising: a computer having a processor and a memory, the memory having executable code stored thereon for execution by the processor for performing the steps of obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the patient.

The present invention also provides a diagnostic tool for predicting disease susceptibility in a patient comprising a plurality of genes capable of predicting p53 mutational status immobilized on a solid support. The solid support may be a microarray, for example. In one embodiment, the plurality of genes immobilized on the solid support may include genes selected from the group consisting of GenBank accession numbers: AI961235, BG271923, NM_(—)002466, BC001651, D38553, AK000345, AA742697, AL080170, BF245284, BC004504, H15261, NM_(—)000909, NM_(—)024843, R73030, NM_(—)030896, AI435828, AL512727, AW242997, AI810764, AI922323, AL360204, NM_(—)003225, NM_(—)003226, AW299538, NM_(—)003462, AI990465, NM_(—)004392, NM_(—)001267, AF269087, AI826437, AL355392 and AU156421. In another embodiment, the plurality of genes immobilized on the solid support may include genes selected from the group consisting of GenBank accession numbers: BG271923, NM_(—)002466, D38553, NM_(—)000909, NM_(—)024843, R73030, NM_(—)003226, AW299538 and AI990465. In yet another embodiment, the plurality of genes immobilized on the solid support may include genes selected from the group consisting of GenBank accession numbers: AI961235, BG271923, NM_(—)002466, BC001651, D38553, AK000345, BC004504, NM_(—)000909, NM_(—)024843, R73030, AI435828, AI810764, AI922323, NM_(—)003225, NM_(—)003226, AW299538, NM_(—)003462, AI990465, NM_(—)004392, NM_(—)001267 and AI826437. In a still further embodiment, the plurality of genes immobilized on the solid support may include genes selected from the group consisting of GenBank accession numbers: NM_(—)002466, BC001651, D38553, NM_(—)024843, AI435828, AI810764, NM_(—)003226 and AW299538.

The present invention also provides a nucleic acid array for predicting disease susceptibility in a patient comprising a solid support and displayed thereon nucleic acid probes corresponding to genes capable of predicting p53 mutational status in the patient. The nucleic acid array may comprise at least 8, 32, 100, 250 or 500 nucleic acid probes.

Thus, the disclosed methods, systems and compositions are capable of discerning p53-deficient from p53-enabled breast tumors and may be effective in gauging p53 activity in other cancer types. As much as 14% of breast tumors that are otherwise p53 wildtype at the DNA sequence level may be deficient for p53 by other means. Moreover, the classifier is a significant predictor of disease-specific survival and recurrence in various breast cancer populations and therefore will have clinical utility in predicting these endpoints, particularly in the context of therapeutic agents that function predominantly through p53-dependent cell death pathways.

EXAMPLES Example 1 The Molecular Configurations of p53 Mutant and p53 Wildtype Tumors are Distinct

To gain insight into the molecular variation between p53 mutant (mt) and p53 wildtype (wt) breast tumors, high-density oligonucleotide microarrays were utilized to analyze a population-based series of 257 biopsies, all of which were previously sequenced for mutations in the p53 coding regions (Bergh, J., Norberg, T., Sjogren, S., Lindgren, A. & Holmberg, L. Complete sequencing of the p53 gene provides prognostic information in breast cancer patients, particularly in relation to adjuvant systemic therapy and radiotherapy. Nat Med 1, 1029-34 (1995), incorporated herein by reference).

The original patient material consisted of freshly frozen breast tumors from a population-based cohort of 315 women representing 65% of all breast cancers resected in Uppsala County during the time period Jan. 1, 1987 to Dec. 31, 1989 (Bergh et al., previously incorporated by reference). After surgery, the viable part of the fresh tumor was cut in two; one part was immediately frozen in isopentane and stored at −70° C. until analysis, and the other was fixed in 10% formalin and prepared for histopathologic examination. Frozen tumor tissue was available from 299 of the original 315 patients. Out of these, 270 had RNA of sufficient quantity and quality for microarray experiments, and after Affymetrix quality control, expression profiles of 260 tumors were further analysed. The present study was approved by the ethical committee at the Karolinska Institute.

Mutational analysis of the p53 gene (TP53) was carried out in the original 315 tumors as described previously in Bergh et al. (previously incorporated by reference). Among the 260 tumors included in the present study, 59 had p53 mutations found by cDNA sequence analysis of exons 2 to 11 (Bergh et al., previously incorporated by reference). In three samples p53 status could not be evaluated. Clinico-pathological characteristics were derived from the patient records and from routine clinical measurements at the time of diagnosis. Estrogen receptor status was determined by ligand binding assay as part of the routine clinical procedure. An experienced pathologist determined the Elston-Ellis grades of the tumors, classifying the tumors into low, medium and high-grade tumors (Elston, C. W. & Ellis, I. O. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19, 403-10 (1991), incorporated herein by reference). Axillary lymph node metastases were found in 84 of these 260 patients while 166 were node-negative. Ten patients had unknown node status, as no axillary examination was performed due to advanced age or concomitant serious disease. Systemic adjuvant therapy was offered to all node-positive patients. In general, premenopausal women were offered chemotherapy and postmenopausal women received endocrine treatment. Out of the 260 patients included in the present study, 149 did not receive adjuvant therapy. Overall survival of the patients was based on information from the Swedish population registry, and date and cause of death were obtained from a review of the patient records in late 1999.

RNA from 59 tumors known to contain p53 mutations resulting in amino acid-level alterations, and from 198 tumors known to have wildtype p53 were analyzed on Affymetrix U133A and U133B arrays.

Extraction of total RNA was carried out using the Qiagen RNeasy Mini Kit (Qiagen, Germany). Frozen tumors were cut into small pieces and homogenized for around 30-40 seconds in test tubes (maximum 40 mg/tube) containing RLT buffer (RNeasy lysis buffer) with mercaptoethanol. The mixtures were then treated with Proteinase K for 10 minutes at 55° C., which in previous RNA extractions demonstrated improved RNA yield (Egyhazi, S. et al. Proteinase K added to the extraction procedure markedly increases RNA yield from primary breast tumors for use in microarray studies. Clin Chem 50, 975-6 (2004), incorporated herein by reference). In the following centrifugation steps on RNeasy columns, DNase treatment was also included to increase the RNA quality. The integrity of the RNA extracts was tested on an Agilent 2100 Bioanalyzer (Agilent Technologies, Rockville, Md., U.S.A), measuring the 28S:18S ribosomal RNA ratio. RNA extracts of high quality were stored at −70° C. until microarray analysis.

Preparation of in vitro transcription (NT) products (i.e., target) and oligonucleotide array hybridization and scanning were performed according to the Affymetrix protocol (Affymetrix Inc., Santa Clara, Calif., U.S.A). First-strand cDNA was synthesized from a starting amount of 2-5 μg total RNA using a T7-linked oligo-dT primer, followed by second-strand synthesis. Double-stranded cDNA was purified using phenol/chloroform extraction and phase lock gel. Biotinylated cRNA targets were prepared from the cDNA templates in NT reactions. The labeled cRNA targets were purified using Qiagen RNeasy Mini Kit and subsequently chemically fragmented. Ten μg of the fragmented, biotinylated cRNA was hybridized to the Affymetrix oligonucleotide human array set, HG-U133A&B, which contains 45,000 probe sets representing more than 39,000 transcripts derived from approximately 33,000 well-substantiated human genes. Hybridization was carried out in a hybridization oven at 45° C. and rotation was set at 60 rpm for 16 h. The arrays were washed and stained in the Fluidics Station 400 (Affymetrix Inc., Santa Clara, Calif., U.S.A) in accordance with the Affymetrix protocol. Staining was carried out using streptavidin-phycoerythrin (SAPE, final concentration of 10 μg/ml) and signal amplification with a biotinylated anti-streptavidin antibody and a second SAPE staining. The arrays were washed and scanned according to the manufacturer's instructions.

The raw expression data was processed using Microarray Suite 5.0 software (Affymetrix Inc., Santa Clara, Calif., U.S.A) and normalized using the global mean method. For each microarray, probeset signal values were scaled by adjusting the mean log intensity to a target signal value of 500. Samples with suboptimal average signal intensities were re-labeled and re-hybridized on new arrays. If microarray artifacts were visible, the samples were re-hybridized on new chips using the same fragmented probe, or alternatively, if the defective areas were small, the affected probes were censored from further analysis. The normalized expression data from both U133A and B chips were combined and natural log transformed.

The extent to which gene expression patterns could distinguish p53 mt and wt tumors was first investigated. By Wilcoxon rank-sum test 3,330 Affymetrix probe-sets representing ˜2,770 distinct genes (according to UniGene build #167) were identified whose expression patterns distinguished p53 mt and wt tumors with a false discovery rate (FDR)-adjusted p value of p<0.001. A number of these genes were found to be known transcriptional targets of p53 including PERP, RRM2, SEMA3B, TAP1, GTSE1, CHECK1, and CHEK2. Shown in FIG. 1 is the result of hierarchical cluster analysis using the top 250 genes, all of which are associated with p53 status with FDR p<5.9×10⁻⁸. As expected from the gene selection criteria, the majority of p53 mt and wt tumors clustered into separate tumor groups. Of two predominant cluster nodes, 90% of the p53 mutants were found in one cluster (i.e., the “mutant-like” cluster), while 77% of p53 wt tumors segregated with the other (the “wildtype-like” cluster).

The hierarchical structure of the gene expression profiles was next investigated. As in the tumors, two predominant clusters were observed: one consisting of ˜200 genes more highly expressed in the mutant-like tumor cluster, and the other representing ˜50 genes more highly expressed in the wildtype-like cluster. Within the former, the genes most highly correlated with p53 mutant status were associated with cell cycle progression including, CDC2, CDC20, CCNB1, CCNB2, CKS2, CDCA1, CDCA3, CDCA8, CENPA, TOP2A, PTTG1 and MCM6. This finding is consistent with the observation that wt p53 has a negative regulatory effect on cell cycle genes. Of the genes more highly expressed in the wildtype-like cluster, the presence of several estrogen-regulated and ER status-associated genes including STC2, NCOR1, and ADRA2A was observed.

Further examination of the tumors revealed that in addition to p53 status, the predominant tumor clusters were also correlated with other clinical features, namely estrogen receptor (ER) status and tumor grade. The estrogen receptor status of a cell has been found to be correlated with cancer in several instances. Normal breast cells usually have receptors for estrogen. However, cancer cells arising in the breast do not always have receptors for estrogen. Breast cancers that have estrogen receptors are said to be “estrogen receptor-positive,” while those breast cancers that do not possess estrogen receptors are “estrogen receptor-negative.” In estrogen receptor-positive cancers, cancer cell growth is under the control of estrogen. In contrast, the growth of estrogen receptor-negative cancer cells is not governed by estrogen.

FIG. 1 shows hierarchical clustering of 257 tumors using the top 250 genes statistically correlated with p53 status. Tumors are represented in columns, genes are represented in rows. The degree of color saturation reflects the magnitude of the log expression signal; red hues denote higher expression levels while green hues indicate lower expression levels. The top row of black vertical bars indicates which breast tumors possess p53 mutations. The second row of bars indicates tumors that are ER positive. The third row of bars reflects histologic grade (Elston-Ellis grading system); green bars=grade I, blue bars=grade II, and red bars=grade III.

Segregating with the mutant-like cluster were observed 86% of estrogen receptor-negative (ER-) tumors)(p_(cs)=1.7×10⁻¹⁰), 96% of grade III tumors (p_(cs)=2.5×10⁻¹⁹) and only 3% of grade I tumors (p_(fe)=6.9×10⁻¹⁵). This result owes, in part, to the fact that the p53 mutants in this study are positively correlated with ER negativity (p_(cs)=1.7×10⁻⁶) and grade III status (p_(cs)=1.2×10⁻¹¹), and is consistent with previous reports demonstrating that p53 mutant breast cancers are significantly correlated with negative ER status and higher tumor grade. See for example, Cattoretti, G., Rilke, F., Andreola, S., D′Amato, L. & Delia, D. P53 expression in breast cancer. Int J Cancer 41, 178-83 (1988); Isola, J., Visakorpi, T., Holli, K. & Kallioniemi, O. P. Association of overexpression of tumor suppressor protein p53 with rapid cell proliferation and poor prognosis in node-negative breast cancer patients. J Natl Cancer Inst 84, 1109-14 (1992); Andersen, T. I. et al. Prognostic significance of TP53 alterations in breast carcinoma. Br J Cancer 68, 540-8 (1993) and Bhargava, V. et al. The association of p53 immunopositivity with tumor proliferation and other prognostic indicators in breast cancer. Mod Pathol 7, 361-8(1994), all of which are incorporated herein by reference.

However, it was also observed that among the p53 wt tumors within the mutant-like cluster, there, too, was a significant over-representation of ER-(p_(cs)=2.0×10⁻⁶) and grade III tumors (p_(fe)=7.1×10⁻¹¹). Thus, by univariate statistical analysis, a large number of genes highly associated with p53 status have been identified that are capable of segregating tumors in a manner correlated with p53 status, but also histologic grade and ER status.

Example 2 A Gene Expression Classifier for Predicting p53 Deficiency.

The finding that a fraction of p53 wt tumors were found to cluster together with the majority of p53 mutants suggests the possibility that these tumors may in fact be p53 deficient through mechanisms other than p53 mutation. Conversely, the discovery of p53 mutants with molecular configurations reminiscent of most wt tumors suggests that these tumors might in fact express functionally intact p53. However, the tumor group assignments in this. case were based on genes selected by a univariate ranking procedure that did not account for the association of p53 status with ER and grade status. This raised the possibility that, to some extent, the selected genes included those that are mostly grade and/or ER associated, which may have biased the clustering of the tumors towards these properties rather than p53 status, per se.

Therefore, a robust gene expression-based classifier for predicting p53 status was developed by designing a predictive model including a multivariate linear regression method known as linear model-fit (LMF) for ranking p53 status-correlated genes independent of histologic grade and ER status.

FIG. 2 shows optimization and results of a gene-based classifier for p53 status. Diagonal Linear Discriminant Analysis (DLDA) was employed for the supervised learning of p53 status using gene expression profiles ranked by the Linear Model-Fit method. (A): Analysis of overlap between grade/estrogen receptor (ER)-correlated genes and p53-correlated genes ranked by Wilcoxon rank-sum test or Linear Model fit. The heat maps indicate the number of genes correlated with tumor grade (upper heat map) or ER status (lower heat map) in 100-gene bins (rows) and also correlated with p53 status (columns; ranked in 50-gene bins); p53 correlated genes were ranked by LMF=Linear Model-Fit or WR=Wilcoxon rank-sum; grade correlated genes were ranked by KW=Kruskal-Wallis, and ER correlated genes by WR. (B): The accuracy of the classifier is plotted as a function of the number of genes used to build the classifier; the optimal classifier consisted of 32 genes and misclassified a total of 40 tumors. (C): The results of the classifier applied to the Uppsala dataset (257 tumors) using leave-one-out cross validation. Unigene symbols (build #167), Genbank accession numbers, and Affymetrix probe IDs (A.=U133A; B.=U133B) are shown.

For gene selection, a linear model was fitted to the gene expression data with expression level as the response, and p53 status, ER status and grade status as the predictor variables. As an initial filter for removing genes not well correlated with the predictor variables, all genes with a p-value fit greater than 0.001 were excluded. Using ER and grade as additional predictors allowed for filtering out genes whose expression patterns could be mostly explained by either ER or grade status. When applied, the LMF ranking procedure markedly reduced the rank of many known cell cycle-regulated genes compared to the univariate Wilcoxon rank-sum (WR) method, indicating that these genes are best explained by high grade rather than p53 status (FIG. 2A, upper panel). Conversely, it was observed that ER-associated genes moved up in the top ranked p53-associated genes by LMF, presumably because their lower ranking by WR resulted from a large number of more highly ranked grade-associated genes (FIG. 2A, lower panel).

For class prediction purposes, the genes were ranked in decreasing order of the absolute value of the p53 status coefficient. For building the classifier, a variant of the maximum likelihood method, DLDA (diagonal linear discriminant analysis) was employed. This had previously been applied to class determination problems using microarray data, described for example, in Dudoit, S., Frilyand, J. & Speed, T. P. Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77-87 (2002), incorporated herein by reference. The set of predictor genes with greatest classification accuracy was chosen by leave-one-out cross validation.

The accuracy of the classifier as a function of the number of genes it comprised is plotted in FIG. 2B. Of particular note was the observation that the accuracy of the tumor classification was highly stable, varying by only 2.7% (i.e., 7 tumors) regardless of whether the classifier comprised 7 genes or 500 genes. Genes in the 500-gene classifier are shown in Table 1 below. The optimal classifier, however, was achieved at 32 genes (Table 2), whereby 40 tumors (15.6%) were misclassified. 28 of the wt tumors (14%) were classified as mutant-like, while 12 mutants (20%) were misclassified as wildtype-like (FIG. 2C).

TABLE 1 Genbank UniGene Rank Affymetrix (decimals Cluster ID UniGene Order Probeset ID removed) (build #173) UniGene Name Symbol 1 A.217889_s_at NM_024843 Hs.31297 cytochrome b reductase 1 CYBRD1 2 B.243929_at H15261 Hs.21948 Transcribed sequences 3 B.229975_at AI826437 Hs.283417 Transcribed sequences 4 B.223864_at AF269087 Hs.326736 ankyrin repeat domain 30A ANKRD30A 5 B.227081_at AW299538 Hs.75528 nucleolar GTPase HUMAUAN TIG 6 A.215014_at AL512727 Hs.232127 MRNA; cDNA DKFZp547P042 (from clone DKFZp4547P042) 7 A.206869_at NM_001267 Hs.97220 Chondroadherin CHAD 8 A.221585_at BC004504 Hs.331904 calcium channel, voltage- CACNG4 dependent, gamma subunit 4 9 A.205440_s_at NM_000909 Hs.519057 neuropeptide Y receptor Y1 NPY1R 10 B.228969_at AI922323 Hs.226391 anterior gradient 2 homolog AGR2 (Xenopus laevis) 11 A.212949_at D38553 Hs.308045 barren homolog (Drosophila) BRRN1 12 B.226067_at AL355392 Data not found 13 B.232855_at AL360204 Hs.283853 MRNA full length insert cDNA clone EUROIMAGE 980547 14 A.221520_s_at BC001651 Hs.48855 Cell division cycle associated 8 CDCA8 15 A.205472_s_at NM_004392 Hs.63931 Dachshund homolog 1 DACH1 (Drosophila) 16 A.205186_at NM_003462 Hs.406050 Dynein, axonemal, light DNALI1 intermediate Polypeptide 1 17 A.221275_s_at NM_030896 Data not found 18 B.229030_at AW242997 Data not found 19 B.233413_at AU156421 Hs.518736 CDNA FLJ13457 fis, clone PLACE1003343 20 A.203438_at AI435828 Hs.155223 stanniocalcin 2 STC2 21 B.230378_at AA742697 Hs.62492 secretoglobin, family 3A, SCGB3A1 member 1 22 B.238581_at BG271923 Hs.237809 guanylate binding protein 5 GBP5 23 B.235343_at AI961235 Hs.96885 hypothetical protein FLJ12505 FLJ12505 24 B.229150_at AI810764 Hs.102406 Transcribed sequences 25 A.205734_s_at AI990465 Hs.38070 lymphoid nuclear protein related LAF4 to AF4 26 A.214079_at AK000345 Hs.272499 Dehydrogenase/reductase (SDR DHRS2 family) member 2 27 B.238746_at BF245284 Hs.354427 Transcribed sequence with weak similarity to protein ref: NP_286085.1 (E. coli) beta-D-galactosidase [Escherichia coli O157:H7 EDL933] 28 A.204623_at NM_003226 Data not found 29 B.230863_at R73030 Hs.252938 low density lipoprotein-related LRP2 protein 2 30 A.215047_at AL080170 Data not found 31 A.201710_at NM_002466 Hs.179718 v-myb myeloblastosis viral MYBL2 oncogene homolog (avian)-like 2 32 A.205009_at NM_003225 Data not found 33 A.207750_at NM_018510 Data not found 34 B.237339_at AI668620 Hs.144151 Transcribed sequences 35 A.220540_at NM_022358 Hs.528664 potassium channel, subfamily K, KCNK15 member 15 36 B.223062_s_at BC004863 Hs.286049 phosphoserine aminotransferase 1 PSAT1 37 A.204508_s_at BC001012 Hs.512620 carbonic anhydrase XII CA12 38 A.214451_at NM_003221 Hs.33102 transcription factor AP-2 beta TFAP2B (activating enhancer binding protein 2 beta) 39 A.202870_s_at NM_001255 Hs.82906 CDC20 cell division cycle 20 CDC20 homolog (S. cerevisiae) 40 B.236641_at AW183154 Hs.3104 kinesin family member 14 KIF14 41 A.219197_s_at AI424243 Hs.435861 signal peptide, CUB domain, SCUBE2 EGF-like 2 42 A.207183_at NM_006143 Hs.92458 G protein-coupled receptor 19 GPR19 43 A.220414_at NM_017422 Hs.180142 calmodulin-like 5 CALML5 44 A.205354_at NM_000156 Hs.81131 guanidinoacetate N- GAMT methyltransferase 45 A.201755_at NM_006739 Hs.77171 MCM5 minichromosome MCM5 maintenance deficient 5, cell division cycle 46 (S. cerevisiae) 46 A.209459_s_at AF237813 Hs.1588 4-aminobutyrate ABAT aminotransferase 47 B.225516_at AA876372 Hs.432978 solute carrier family 7 (cationic SLC7A2 amino acid transporter, y+ system), member 2 48 A.204558_at NM_003579 Hs.66718 RAD54-like (S. cerevisiae) RAD54L 49 B.224428_s_at AY029179 Hs.435733 cell division cycle associated 7 CDCA7 50 B.228854_at AI492388 Hs.356349 zinc finger protein 145 ZNF145 (Kruppel-like, expressed in promyelocytic leukemia) 51 A.208502_s_at NM_002653 Hs.84136 paired-like homeodomain PITX1 transcription factor 1 52 B.226936_at BG492359 Hs.35962 CDNA clone IMAGE: 4448513, partial cds 53 B.230021_at AI638593 Hs.441708 hypothetical protein MGC45866 MGC45866 54 A.206799_at NM_006551 Hs.204096 secretoglobin, family 1D, SCGB1D2 member 2 55 A.202410_x_at NM_000612 Hs.349109 insulin-like growth factor 2 IGF2 (somatomedin A) 56 A.206509_at NM_002652 Hs.99949 prolactin-induced protein PIP 57 A.204885_s_at NM_005823 Hs.408488 Mesothelin MSLN 58 A.201496_x_at AI889739 Hs.78344 myosin, heavy polypeptide 11, MYH11 smooth muscle 59 A.206401_s_at J03778 Hs.101174 microtubule-associated protein MAPT tau 60 A.204734_at NM_002275 Hs.80342 keratin 15 KRT15 61 A.204014_at NM_001394 Hs.417962 dual specificity phosphatase 4 DUSP4 62 A.204775_at NM_005441 Hs.75238 chromatin assembly factor 1, CHAF1B subunit B (p60) 63 A.215356_at AK023134 Hs.130675 hypothetical gene FLJ13072 FLJ13072 64 B.243049_at AI791225 Hs.444098 MRNA; cDNA DKFZp434I1226 from clone DKFZp434I1226) 65 B.223721_s_at AF176013 Hs.260720 DnaJ (Hsp40) homolog, DNAJC12 subfamily C, member 12 66 A.219918_s_at NM_018123 Data not found 67 B.243735_at N58363 Hs.8739 signal transducer and activator STATIP1 of transcription 3 interacting protein 1 68 A.214188_at AW665096 Hs.15299 HMBA-inducible HIS1 69 B.226980_at AK001166 Hs.421337 DEP domain containing 1B DEPDC1B 70 A.203071_at NM_004636 Hs.82222 sema domain, immunoglobulin SEMA3B domain (Ig), short basic domain, secreted, (semaphorin) 3B 71 A.206204_at NM_004490 Hs.411881 growth factor receptor-bound GRB14 protein 14 72 A.205979_at NM_002407 Hs.97644 secretoglobin, family 2A, SCGB2A1 member 1 73 A.208335_s_at NM_002036 Hs.517102 Duffy blood group FY 74 B.227550_at AW242720 Hs.388347 MRNA; cDNA DKFZp686J0156 (from clone DKFZp686J0156) 75 A.220187_at NM_024636 Hs.44208 likely ortholog of mouse tumor FLJ23153 necrosis-alpha-induced adipose- related protein 76 B.226473_at BE514414 Hs.103305 hypothetical protein MGC10561 MGC10561 77 A.204822_at NM_003318 Hs.169840 TTK protein kinase TTK 78 A.204724_s_at NM_001853 Hs.126248 collagen, type IX, alpha 3 COL9A3 79 A.205240_at NM_013296 Hs.278338 G-protein signalling modulator 2 GPSM2 (AGS3-like, C. elegans) 80 A.205898_at U20350 Hs.78913 chemokine (C—X3—C motif) CX3CR1 receptor 1 81 B.223381_at AF326731 Hs.234545 cell division cycle associated 1 CDCA1 82 A.209243_s_at AF208967 Hs.201776 paternally expressed 3 PEG3 83 A.204146_at BE966146 Data not found 84 B.228273_at BG165011 Hs.528654 hypothetical protein FLJ11029 FLJ11029 85 A.204162_at NM_006101 Hs.414407 kinetochore associated 2 KNTC2 86 A.204914_s_at AI360875 Hs.432638 SRY (sex determining region SOX11 Y)-box 11 87 A.209309_at D90427 Hs.512643 alpha-2-glycoprotein 1, zinc AZGP1 88 A.205048_s_at NM_003832 Data not found 89 B.227419_x_at AW964972 Hs.361171 placenta-specific 9 PLAC9 90 B.232944_at AK024132 Hs.525858 MRNA; cDNA DKFZp686I18125 (from clone DKFZp686I18125) 91 B.224753_at BE614410 Hs.434886 cell division cycle associated 5 CDCA5 92 A.210051_at U78168 Hs.8578 Rap guanine nucleotide RAPGEF3 exchange factor (GEF) 3 93 A.215616_s_at AB020683 Hs.301011 jumonji domain containing 2B JMJD2B 94 A.210272_at M29873 Hs.415794 cytochrome P450, family 2, CYP2B7 subfamily B, polypeptide 7 pseudogene 95 B.222608_s_at AK023208 Hs.62180 anillin, actin binding protein ANLN (scraps homolog, Drosophila) 96 B.240724_at AI668629 Hs.25345 Transcribed sequences 97 B.228554_at AL137566 Hs.32405 MRNA; cDNA DKFZp686A0815 (from clone DKFZp686A0815) 98 A.205280_at NM_000824 Hs.32973 glycine receptor, beta GLRB 99 B.238659_at AA760689 Hs.210532 KIAA0141 gene product KIAA0141 100 B.238116_at AW959427 Hs.98849 dynein, cytoplasmic, light DNCL2B polypeptide 2B 101 A.212448_at AB007899 Hs.249798 neural precursor cell expressed, NEDD4L developmentally down-regulated 4-like 102 B.235572_at AI469788 Hs.381225 kinetochore protein Spc24 Spc24 103 A.209603_at AI796169 Hs.169946 GATA binding protein 3 GATA3 104 A.205358_at NM_000826 Hs.335051 glutamate receptor, ionotropic, GRIA2 AMPA 2 105 A.202095_s_at NM_001168 Hs.1578 baculoviral IAP repeat- BIRC5 containing 5 (survivin) 106 A.211470_s_at AF186255 Hs.38084 sulfotransferase family, SULT1C1 cytosolic, 1C, member 1 107 A.205350_at NM_004378 Hs.346950 cellular retinoic acid binding CRABP1 protein 1 108 A.205890_s_at NM_006398 Hs.44532 ubiquitin D UBD 109 A.209680_s_at BC000712 Hs.20830 kinesin family member C1 KIFC1 110 B.240192_at AI631850 Hs.158992 FLJ45983 protein FLJ45983 111 A.205225_at NM_000125 Hs.1657 estrogen receptor 1 ESR1 112 B.235545_at AI810054 Hs.445098 DEP domain containing 1 DEPDC1 113 B.224210_s_at BC001147 Hs.436924 peroxisomal membrane protein PXMP4 4, 24 kDa 114 B.229381_at AI732488 Hs.29190 hypothetical protein MGC24047 MGC24047 115 A.210523_at D89675 Hs.87223 bone morphogenetic protein BMPR1B receptor, type IB 116 A.204641_at NM_002497 Hs.153704 NIMA (never in mitosis gene a)- NEK2 related kinase 2 117 B.227764_at AA227842 Hs.21929 hypothetical protein MGC52057 MGC52057 118 B.238900_at BE669692 Data not found 119 A.202580_x_at NM_021953 Hs.511941 forkhead box M1 FOXM1 120 A.205366_s_at NM_018952 Hs.147465 homeo box B6 HOXB6 121 B.227966_s_at AA524895 Hs.449141 Hypothetical protein LOC285103, mRNA (cDNA clone IMAGE: 5273139), partial cds 122 B.228069_at AL138828 Data not found 123 A.210163_at AF030514 Hs.103982 chemokine (C—X—C motif) ligand CXCL11 11 124 A.204855_at NM_002639 Hs.55279 serine (or cysteine) proteinase SERPINB5 inhibitor, clade B (ovalbumin), member 5 125 B.229390_at AV734646 Hs.381220 Full length insert cDNA clone ZA84A12 126 A.203213_at AL524035 Hs.334562 cell division cycle 2, G1 to S and CDC2 G2 to M 127 A.219555_s_at NM_018455 Hs.283532 uncharacterized bone marrow BM039 protein BM039 128 B.227282_at AB037734 Hs.4993 protocadherin 19 PCDH19 129 A.220085_at NM_018063 Hs.203963 helicase, lymphoid-specific HELLS 130 A.203256_at NM_001793 Hs.191842 cadherin 3, type 1, P-cadherin CDH3 (placental) 131 B.234992_x_at BG170335 Hs.293257 epithelial cell transforming ECT2 sequence 2 oncogene 132 A.204825_at NM_014791 Hs.184339 maternal embryonic leucine MELK zipper kinase 133 A.204126_s_at NM_003504 Hs.114311 CDC45 cell division cycle 45- CDC45L like (S. cerevisiae) 134 A.218663_at NM_022346 Hs.528669 chromosome condensation HCAP-G protein G 135 B.239962_at AA972452 Hs.292072 Transcribed sequences 136 A.205046_at NM_001813 Hs.75573 centromere protein E, 312 kDa CENPE 137 B.235717_at AA180985 Hs.285574 zinc finger protein 229 ZNF229 138 B.233154_at AK022197 Hs.130581 CDNA FLJ12135 fis, clone MAMMA1000307 139 A.206754_s_at NM_000767 Hs.1360 cytochrome P450, family 2, CYP2B6 subfamily B, polypeptide 6 140 A.204533_at NM_001565 Hs.413924 chemokine (C—X—C motif) ligand CXCL10 10 141 A.212925_at AA143765 Hs.439180 chromosome 19 open reading C19orf21 frame 21 142 B.223229_at AB032931 Hs.5199 HSPC150 protein similar to HSPC150 ubiquitin-conjugating enzyme 143 A.206599_at NM_004695 HS.90911 solute carrier family 16 SLC16A5 (monocarboxylic acid transporters), member 5 144 A.208103_s_at NM_030920 Hs.385913 acidic (leucine-rich) nuclear ANP32E phosphoprotein 32 family, member E 145 A.217953_at AW189430 Hs.348921 PHD finger protein 3 PHF3 146 A.219686_at NM_018401 Hs.58241 serine/threonine kinase 32B STK32B 147 A.217276_x_at AL590118 Hs.301947 kraken-like dJ222E13.1 148 B.234863_x_at AK026197 Hs.272027 F-box protein 5 FBXO5 149 B.240465_at BF508074 Data not found 150 A.218308_at NM_006342 Hs.104019 transforming, acidic coiled-coil TACC3 containing protein 3 151 A.206157_at NM_002852 Hs.2050 pentaxin-related gene, rapidly PTX3 induced by IL-1 beta 152 A.209368_at AF233336 Hs.212088 epoxide hydrolase 2, EPHX2 cytoplasmic 153 B.230856_at AI073396 Hs.9398 WD40 repeat protein Interacting WIPI49 with phosphoInositides of 49 kDa 154 A.201890_at NM_001034 Hs.226390 ribonucleotide reductase M2 RRM2 polypeptide 155 A.205364_at NM_003500 Hs.9795 acyl-Coenzyme A oxidase 2, ACOX2 branched chain 156 B.225911_at AL138410 Hs.282832 hypothetical protein LOC255743 LOC255743 157 B.244696_at AI033582 Hs.372254 Transcribed sequences 158 A.2187301_s_at NM_014057 Hs.109439 osteoglycin (osteoinductive OGN factor, mimecan) 159 A.219498_s_at NM_018014 Hs.314623 B-cell CLL/lymphoma 11A BCL11A (zinc finger protein) 160 A.203702_s_at AL043927 Hs.169910 tubulin tyrosine ligase-like TTLL4 family, member 4 161 A.206045_s_at NM_003787 Hs.23567 nucleolar protein 4 NOL4 162 A.219919_s_at NM_018276 Hs.29173 slingshot homolog 3 SSH3 (Drosophila) 163 A.215779_s_at BE271470 Data not found 164 B.230966_at AI859620 Hs.437023 interleukin 4 induced 1 IL4I1 165 A.206378_at NM_002411 Hs.46452 secretoglobin, family 2A, SCGB2A2 member 2 166 A.221562_s_at AF083108 Hs.511950 sirtuin (silent mating type SIRT3 information regulation 2 homolog) 3 (S. cerevisiae) 167 A.221258_s_at NM_031217 Hs.301052 kinesin family member 18A DKFZP434G2226 168 A.221577_x_at AF003934 Hs.296638 growth differentiation factor 15 GDF15 169 B.235709_at H37811 Hs.20575 growth arrest-specific 2 like 3 GAS2L3 170 B.235171_at AI354636 Data not found 171 A.207437_at NM_006491 Hs.292511 neuro-oncological ventral NOVA1 antigen 1 172 A.203638_s_at NM_022969 Hs.404081 fibroblast growth factor receptor 2 FGFR2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome, Pfeiffer syndrome, Jackson- Weiss syndrome) 173 A.218542_at NM_018131 Hs.14559 chromosome 10 open reading C10orf3 frame 3 174 A.217613_at AW173720 Hs.176227 hypothetical protein FLJ11155 FLJ11155 175 B.241310_at AI685841 Hs.161354 Transcribed sequences 176 A.205234_at NM_004696 Hs.351306 solute carrier family 16 SLC16A4 (monocarboxylic acid transporters), member 4 177 A.203726_s_at NM_000227 Hs.83450 laminin, alpha 3 LAMA3 178 A.221436_s_at NM_031299 Hs.30114 cell division cycle associated 3 CDCA3 179 A.205242_at NM_006419 Hs.100431 chemokine (C—X—C motif) ligand CXCL13 13 (B-cell chemoattractant) 180 A.218726_at NM_018410 Hs.104859 hypothetical protein DKFZp762E1312 DKFZp762E1312 181 A.218856_at NM_016629 Data not found 182 B.226661_at T90295 Data not found 183 A.218741_at NM_024053 Hs.208912 chromosome 22 open reading C22orf18 frame 18 184 A.206201_s_at NM_005924 Hs.77858 mesenchyme homeo box 2 MEOX2 (growth arrest-specific homeo box) 185 B.236184_at AI798959 Hs.131686 Transcribed sequences 186 A.220651_s_at NM_018518 Hs.198363 MCM10 minichromosome MCM10 maintenance deficient 10 (S. cerevisiae) 187 A.216331_at AK022548 Hs.74369 integrin, alpha 7 ITGA7 188 B.232105_at AU148391 Hs.181245 MRNA; cDNA DKFZp686B15184 (from clone DKFZp686B15184) 189 B.226907_at N32557 Hs.192822 protein phosphatase 1, PPP1R14C regulatory (inhibitor) subunit 14C 190 B.234976_x_at BG324504 Hs.321127 solute carrier family 4, sodium SLC4A5 bicarbonate cotransporter, member 5 191 A.211323_s_at L38019 Hs.149900 inositol 1,4,5-triphosphate ITPR1 receptor, type 1 192 A.206391_at NM_002888 Hs.82547 retinoic acid receptor responder RARRES1 (tazarotene induced) 1 193 A.222348_at AW971134 Hs.212787 KIAA0303 protein KIAA0303 194 B.235845_at AI380207 Hs.368802 Sp5 transcription factor SP5 195 B.239233_at AA744613 Hs.292925 KIAA1212 KIAA1212 196 A.208383_s_at NM_002591 Hs.1872 phosphoenolpyruvate PCK1 carboxykinase 1 (soluble) 197 A.214440_at NM_000662 Hs.155956 N-acetyltransferase 1 (arylamine NAT1 N-acetyltransferase) 198 B.230456_at BE501559 Hs.380824 NS5ATP13TP2 protein NS5ATP13TP2 199 A.219650_at NM_017669 Data not found 200 A.210052_s_at AF098158 Hs.9329 TPX2, microtubule-associated TPX2 protein homolog (Xenopus laevis) 201 A.204468_s_at NM_005424 Hs.78824 tyrosine kinase with TIE immunoglobulin and epidermal growth factor homology domains 202 A.209531_at BC001453 Hs.26403 glutathione transferase zeta 1 GSTZ1 (maleylacetoacetate isomerase) 203 A.217014_s_at AC004522 Data not found 204 B.227155_at R10289 Hs.3844 LIM domain only 4 LMO4 205 A.213520_at NM_004260 Hs.31442 RecQ protein-like 4 RECQL4 206 B.241505_at BF513468 Data not found 207 A.213451_x_at BE044614 Hs.411644 tenascin XB TNXB 208 A.214389_at AI733515 Hs.148907 hypothetical protein MGC52019 MGC52019 209 B.235229_at AI694413 Data not found 210 A.203571_s_at NM_006829 Hs.511763 chromosome 10 open reading C10orf116 frame 116 211 B.237168_at AA708016 Data not found 212 A.203915_at NM_002416 Hs.77367 chemokine (C—X—C motif) ligand 9 CXCL9 213 B.224509_s_at BC006399 Hs.155839 reticulon 4 interacting protein 1 RTN4IP1 214 A.206093_x_at NM_007116 Data not found 215 A.205613_at NM_016524 Hs.258326 B/K protein LOC51760 216 B.236885_at AI651930 Data not found 217 B.236341_at AI733018 Hs.247824 cytotoxic T-lymphocyte- CTLA4 associated protein 4 218 A.221854_at AI378979 Hs.313068 plakophilin 1 (ectodermal PKP1 dysplasia/ skin fragility syndrome) 219 A.201291_s_at NM_001067 Hs.156346 topoisomerase (DNA) II alpha TOP2A 170 kDa 220 B.232734_at AK023230 Hs.139709 hypothetical protein FLJ12572 FLJ12572 221 A.214053_at AW772192 Hs.7888 CDNA FLJ44318 fis, clone TRACH3000780 222 B.231195_at AI492376 Data not found 223 A.212956_at AB020689 Hs.411317 KIAA0882 protein KIAA0882 224 A.214404_x_at AI307916 Hs.79414 SAM pointed domain containing SPDEF ets transcription factor 225 B.237086_at AI693336 Hs.163484 forkhead box A1 FOXA1 226 A.205948_at NM_007050 Hs.225952 protein tyrosine phosphatase, PTPRT receptor type, T 227 A.214745_at AW665865 Hs.193143 KIAA1069 protein KIAA1069 228 A.208029_s_at NM_018407 Hs.296398 lysosomal associated protein LAPTM4B transmembrane 4 beta 229 A.205569_at NM_014398 Hs.10887 lysosomal-associated membrane LAMP3 protein 3 230 B.235046_at AA456099 Hs.176376 Transcribed sequences 231 A.203130_s_at NM_004522 Data not found 232 B.238584_at W52934 Hs.113009 hypothetical protein FLJ22527 FLJ22527 233 A.220986_s_at NM_030953 Hs.169333 tigger transposable element TIGD6 derived 6 234 A.205023_at D14134 Hs.446554 RAD51 homolog (RecA RAD51 homolog, E. coli) (S. cerevisiae) 235 B.237048_at AW451103 Hs.71371 Clone IMAGE: 4797878, mRNA, partial cds 236 B.225400_at BF111780 Hs.440663 chromosome 1 open reading C1orf19 frame 19 237 A.206134_at NM_014479 Hs.145296 ADAM-like, decysin 1 ADAMDEC1 238 A.214469_at NM_021052 Hs.121017 histone 1, H2ae HIST1H2AE 239 A.202188_at NM_014669 Hs.295014 nucleoporin 93 kDa NUP93 240 A.204678_s_at U90065 Hs.376874 potassium channel, subfamily K, KCNK1 member 1 241 B.231517_at AW243917 Hs.196566 ZYG-11A early embryogenesis protein mRNA, complete cds 242 A.210387_at BC001131 Data not found 243 B.223623_at AF325503 Hs.43125 esophageal cancer related gene 4 ECRG4 protein 244 B.228729_at N90191 Hs.23960 cyclin B1 CCNB1 245 A.204904_at NM_002060 Hs.296310 gap junction protein, alpha 4, GJA4 37 kDa (connexin 37) 246 B.237301_at BF433570 Hs.144479 Transcribed sequences 247 B.239623_at N93197 Hs.49573 CDNA FLJ44606 fis, clone BRACE2005991 248 B.242601_at AA600175 Hs.443169 hypothetical protein LOC253012 LOC253012 249 B.223861_at AL136755 Hs.298312 HORMA domain containing NOHMA protein 250 A.213122_at AI096375 Hs.173094 TSPY-like 5 TSPYL5 251 A.204482_at NM_003277 Hs.505337 claudin 5 (transmembrane CLDN5 protein deleted in velocardiofacial syndrome) 252 B.240512_x_at H10766 Hs.23406 potassium channel KCTD4 tetramerisation domain containing 4 253 A.209642_at AF043294 Hs.287472 BUB1 budding uninhibited by BUB1 benzimidazoles 1 homolog (yeast) 254 B.239669_at AW006409 Hs.532143 Transcribed sequences 255 B.243028_x_at BE045392 Data not found 256 A.210721_s_at AB040812 Hs.32539 p21(CDKN1A)-activated kinase 7 PAK7 257 A.215942_s_at BF973178 Hs.122552 G-2 and S-phase expressed 1 GTSE1 258 B.222895_s_at AA918317 Hs.57987 B-cell CLL/lymphoma 11B BCL11B (zinc finger protein) 259 A.203708_at NM_002600 Hs.188 phosphodiesterase 4B, cAMP- PDE4B specific (phosphodiesterase E4 dunce homolog, Drosophila) 260 B.235178_x_at AL120674 Data not found 261 B.236471_at AI949827 Hs.404741 nuclear factor (erythroid-derived NFE2L3 2)-like 3 262 A.220024_s_at NM_020956 Hs.205457 periaxin PRX 263 A.213711_at NM_002281 Hs.170925 keratin, hair, basic, 1 KRTHB1 264 A.204766_s_at NM_002452 Hs.413078 nudix (nucleoside diphosphate NUDT1 linked moiety X)-type motif 1 265 B.227182_at AW966474 Hs.88417 sushi domain containing 3 SUSD3 266 A.220061_at NM_017888 Hs.122939 hypothetical protein FLJ20581 FLJ20581 267 A.220117_at NM_024697 Hs.99256 hypothetical protein FLJ22419 FLJ22419 268 B.237395_at AV700083 Hs.176588 cytochrome P450, family 4, CYP4Z1 subfamily Z, polypeptide 1 269 B.226034_at BE222344 Hs.346735 Clone IMAGE: 3881549, mRNA 270 A.207038_at NM_004694 Hs.42645 solute carrier family 16 SLC16A6 (monocarboxylic acid transporters), member 6 271 B.238541_at BE544855 Hs.236572 CDNA clone IMAGE: 5265729, partial cds 272 A.207702_s_at NM_012301 Hs.22599 atrophin-1 interacting protein 1 AIP1 273 B.236496_at AW006352 Hs.159643 chromosome 14 open reading C14orf66 frame 66 274 A.215300_s_at AK022172 Hs.396595 flavin containing FMO5 monooxygenase 5 275 A.219580_s_at NM_024780 Hs.145807 transmembrane channel-like 5 TMC5 276 B.230469_at AW665138 Hs.58559 pleckstrin homology domain PLEKHK1 containing, family K member 1 277 B.243636_s_at AI042373 Hs.132917 Transcribed sequences 278 A.203764_at NM_014750 Hs.77695 discs, large homolog 7 DLG7 (Drosophila) 279 A.209936_at AF107493 Hs.439480 RNA binding motif protein 5 RBM5 280 A.207961_x_at NM_022870 Data not found 281 B.233059_at AK026384 Hs.199776 potassium inwardly-rectifying KCNJ3 channel, subfamily J, member 3 282 A.221583_s_at AI129381 Hs.354740 potassium large conductance KCNMA1 calcium-activated channel, subfamily M, alpha member 1 283 B.228762_at AW151924 Hs.159142 lunatic fringe homolog LFNG (Drosophila) 284 A.219415_at NM_020659 Hs.268728 tweety homolog 1 (Drosophila) TTYH1 285 A.203397_s_at BF063271 Hs.278611 UDP-N-acetyl-alpha-D- GALNT3 galactosamine:polypeptide N- acetylgalactosaminyltransferase 3(GalNAc-T3) 286 A.206091_at NM_002381 Hs.278461 matrilin 3 MATN3 287 A.217562_at BF589529 Hs.497208 DBCCR1-like DBCCR1L 288 B.229764_at AW629527 Hs.338851 FLJ41238 protein FLJ41238 289 B.232544_at AU144916 Hs.222056 CDNA FLJ11572 fis, clone HEMBA1003373 290 A.203819_s_at AU160004 Hs.79440 IGF-II mRNA-binding protein 3 IMP-3 291 A.206102_at NM_021067 Data not found 292 A.210738_s_at AF011390 Hs.5462 solute carrier family 4, sodium SLC4A4 bicarbonate cotransporter, member 4 293 B.236285_at AI631846 Hs.137007 hypothetical protein BC009980 LOC113730 294 A.209800_at AF061812 Hs.432448 keratin 16 (focal non- KRT16 epidermolytic palmoplantar keratoderma) 295 A.218211_s_at NM_024101 Hs.297405 Melanophilin MLPH 296 B.223361_at AF116682 Hs.238205 chromosome 6 open reading C6orf115 frame 115 297 B.242776_at AA584428 Hs.12742 zinc finger, CCHC domain ZCCHC6 containing 6 298 A.221909_at BF984207 Data not found 299 A.209408_at U63743 Hs.69360 kinesin family member 2C KIF2C 300 A.215812_s_at U41163 Data not found 301 B.232238_at AK001380 Hs.121028 asp (abnormal spindle)-like, ASPM microcephaly associated (Drosophila) 302 B.223126_s_at AF312864 Hs.12532 chromosome 1 open reading C1orf21 frame 21 303 A.212141_at X74794 Hs.460184 MCM4 minichromosome MCM4 maintenance deficient 4 (S. cerevisiae) 304 A.222325_at AW974812 Hs.433049 Transcribed sequences 305 B.224314_s_at AF277174 Hs.130946 egl nine homolog 1 (C. elegans) EGLN1 306 A.207470_at NM_017535 Hs.194369 arginine-glutamic acid dipeptide RERE (RE) repeats 307 B.228504_at AI828648 Hs.406684 sodium channel, voltage-gated, SCN7A type VII, alpha 308 B.228245_s_at AW594320 Hs.405557 ovostatin 2 OVOS2 309 A.213712_at BF508639 Hs.58488 catenin (cadherin-associated CTNNAL1 protein), alpha-like 1 310 A.213998_s_at AW188131 Hs.250696 DEAD (Asp-Glu-Ala-Asp) box DDX17 polypeptide 17 311 B.230323_s_at AW242836 Hs.355663 hypothetical protein BC016153 LOC120224 312 A.212713_at R72286 Hs.296049 microfibrillar-associated protein 4 MFAP4 313 B.230316_at R49343 Hs.430576 SEC14-like 2 (S. cerevisiae) SEC14L2 314 A.32128_at Y13710 Hs.16530 chemolcine (C-C motif) ligand CCL18 18 (pulmonary and activation- regulated) 315 B.236718_at AI278445 Hs.43334 Transcribed sequence with weak similarity to protein sp:P39189 (H. sapiens) ALU2_HUMAN Alu subfamily SB sequence Contamination warning entry 316 B.227030_at BG231773 Hs.371680 CDNA FLJ46579 fis, clone THYMU3042758 317 B.235658_at AW058580 Hs.151444 Transcribed sequences 318 B.230622_at BE552393 Hs.100469 myeloid/lymphoid or mixed- MLLT4 lineage leukemia (trithorax homolog, Drosophila); translocated to, 4 319 A.205213_at NM_014716 Hs.337242 centaurin, beta 1 CENTB1 320 A.221754_s_at AI341234 Hs.6191 coronin, actin binding protein, CORO1B 1B 321 A.214612_x_at U10691 Data not found 322 A.203463_s_at H05668 Hs.7407 epsin 2 EPN2 323 B.237350_at AW027968 Hs.454465 Similar to CDNA sequence BC021608 (LOC143941), mRNA 324 A.220789_s_at NM_004749 Hs.231411 transforming growth factor beta TBRG4 regulator 4 325 A.208496_x_at NM_003534 Hs.247813 histone 1, H3g HIST1H3G 326 A.202992_at NM_000587 Hs.78065 complement component 7 C7 327 A.210432_s_at AF225986 Hs.300717 sodium channel, voltage-gated, SCN3A type III, alpha 328 B.239525_at AI733041 Hs.374649 hypothetical protein DKFZp547A023 DKFZp547A023 329 B.244344_at AW135316 Hs.105448 protein kinase, lysine deficient 4 PRKWNK4 330 B.236773_at AI635931 Hs.147613 Transcribed sequences 331 A.207118_s_at NM_004659 Hs.211819 matrix metalloproteinase 23B MMP23B 332 B.228558_at AL518291 Data not found 333 B.230269_at AI963605 Hs.406256 Transcribed sequences 334 B.228262_at AW237462 Hs.127951 hypothetical protein FLJ14503 FLJ14503 335 B.238878_at AA496211 Hs.157208 aristaless related homeobox ARX 336 B.228559_at BF111626 Hs.55028 CDNA clone IMAGE: 6043059, partial cds 337 A.204542_at NM_006456 Hs.288215 sialyltransferase 7 SIAT7B ((alpha-N-acetylneuraminyl-2,3- beta-galactosyl- 1,3)-N-acetyl galactosaminide alpha-2,6- sialyltransferase) B 338 B.224839_s_at BF310919 Hs.355862 glutamic pyruvate transaminase GPT2 (alanine aminotransferase) 2 339 A.209755_at AF288395 Hs.158244 nicotinamide nucleotide NMNAT2 adenylyltransferase 2 340 B.229019_at AI694320 Hs.6295 zinc finger protein 533 ZNF533 341 A.218039_at NM_016359 Hs.279905 nucleolar and spindle associated NUSAP1 protein 1 342 A.205947_s_at NM_003382 Hs.170560 vasoactive intestinal peptide VIPR2 receptor 2 343 B.244107_at AW189097 Hs.444393 Transcribed sequences 344 B.228241_at AI827789 Hs.100686 breast cancer membrane protein BCMP11 11 345 A.204750_s_at BF196457 Hs.95612 desmocollin 2 DSC2 346 A.204130_at NM_000196 Hs.1376 hydroxysteroid (11-beta) HSD11B2 dehydrogenase 2 347 A.220119_at NM_022140 Hs.104746 erythrocyte membrane protein EPB41L4A band 4.1 like 4A 348 B.230238_at AI744123 Hs.13308 hypothetical protein LOC134548 LOC134548 349 A.204719_at NM_007168 Hs.58351 ATP-binding cassette, sub- ABCA8 family A (ABC1), member 8 350 A.219961_s_at NM_018474 Hs.436632 chromosome 20 open reading C20orf19 frame 19 351 A.219132_at NM_021255 Hs.44038 pellino homolog 2 (Drosophila) PELI2 352 A.220584_at NM_025094 Data not found 353 B.227350_at AI807356 Hs.127797 CDNA FLJ11381 fis, clone HEMBA1000501 354 B.230800_at AV699353 Hs.443428 adenylate cyclase 4 ADCY4 355 A.204709_s_at NM_004856 Hs.270845 kinesin family member 23 KIF23 356 B.243526_at AI968904 Hs.174373 hypothetical protein LOC349136 LOC349136 357 A.219491_at NM_024036 Hs.148438 leucine rich repeat and LRFN4 fibronectin type III domain containing 4 358 A.204686_at NM_005544 Hs.390242 insulin receptor substrate 1 IRS1 359 B.228066_at AI870951 Hs.445574 Transcribed sequence with weak similarity to protein pir: I37984 (H. sapiens) I37984 keratin 9, type I, cytoskeletal- human 360 A.206795_at NM_004101 Hs.42502 coagulation factor II (thrombin) F2RL2 receptor-like 2 361 A.209464_at AB011446 Hs.442658 aurora kinase B AURKB 362 B.229082_at AI141520 Data not found 363 B.240304_s_at BG484769 Hs.115838 CDNA FLJ44282 fis, clone TRACH2003516 364 B.227702_at AA557324 Hs.439760 cytochrome P450, family 4, CYP4X1 subfamily X, polypeptide 1 365 B.235077_at BF956762 Hs.418271 maternally expressed 3 MEG3 366 A.202705_at NM_004701 Hs.194698 cyclin B2 CCNB2 367 A.209616_s_at S73751 Hs.278997 carboxylesterase 1 CES1 (monocyte/macrophage serine esterase 1) 368 A.211441_x_at AF280113 Hs.306220 cytochrome P450, family 3, CYP3A43 subfamily A, polypeptide 43 369 B.241861_at R89089 Data not found 370 B.228425_at BF056746 Hs.516311 MRNA; cDNA DKFZp686E10196 (from clone DKFZp686E10196); complete cds 371 A.213938_at Z38645 Hs.476384 CAZ-associated structural CAST protein 372 A.202409_at X07868 Data not found 373 A.219115_s_at NM_014432 Hs.288240 Interleukin 20 receptor, alpha IL20RA 374 A.39248_at N74607 Hs.234642 Aquaporin 3 AQP3 375 B.227232_at T58044 Data not found 376 B.230319_at AI222435 Hs.90250 CDNA FLJ36413 fis, clone THYMU2010816 377 A.203287_at NM_005558 Hs.18141 Ladinin 1 LAD1 378 A.218009_s_at NM_003981 Hs.344037 Protein regulator of cytokinesis 1 PRC1 379 A.222351_at AW009884 Hs.431156 Protein phosphatase 2 (formerly PPP2R1B 2A), Regulatory subunit A (PR 65), beta isoform 380 A.204794_at NM_004418 Hs.1183 Dual specificity phosphatase 2 DUSP2 381 A.211456_x_at AF333388 Data not found 382 A.206296_x_at NM_007181 Hs.95424 Mitogen-activated protein kinase MAP4K1 kinase Kinase kinase 1 383 A.205357_s_at NM_000685 Hs.197063 Angiotensin II receptor, type 1 AGTR1 384 B.244385_at AA766126 Data not found 385 A.202235_at NM_003051 Hs.75231 Solute carrier family 16 SLC16A1 (monocarboxylic Acid transporters), member 1 386 B.240422_at AI935710 Hs.530456 Transcribed sequences 387 B.230644_at AI375083 Hs.31522 Leucine rich repeat and LRFN5 fibronectin type III Domain containing 5 388 A.220238_s_at NM_018846 Hs.376793 Kelch-like 7 (Drosophila) KLHL7 389 B.235004_at AI677701 Hs.201619 RNA binding motif protein 24 RBM24 390 A.201397_at NM_006623 Hs.3343 Phosphoglycerate PHGDH dehydrogenase 391 A.208010_s_at NM_012411 Hs.87860 Protein tyrosine phosphatase, PTPN22 Non-receptor type 22 (lymphoid) 392 A.210138_at AF074979 Hs.141492 Regulator of G-protein RGS20 signalling 20 393 A.203828_s_at NM_004221 Hs.943 Natural killer cell transcript 4 NK4 394 A.205862_at NM_014668 Hs.438037 GREB1 protein GREB1 395 A.219984_s_at NM_020386 Hs.36761 HRAS-like suppressor HRASLS 396 A.203358_s_at NM_004456 Hs.444082 Enhancer of zeste homolog 2 EZH2 (Drosophila) 397 B.232570_s_at AL356755 Data not found 398 A.212613_at AI991252 Hs.376046 Butyrophilin, subfamily 3, BTN3A2 member A2 399 B.238077_at T75480 Hs.13982 Potassium channel KCTD6 tetramerisation Domain containing 6 400 A.217023_x_at AF099143 Data not found 401 B.242093_at AW263497 Hs.97774 Synaptotagmin-like 5 SYTL5 402 B.232979_at AK000839 Hs.306410 CDNA FLJ20832 fis, clone ADKA03033 403 B.232286_at AA572675 Hs.188173 CDNA FLJ12187 fis, clone MAMMA1000831 404 A.203223_at NM_004703 Hs.390163 Rabaptin, RAB GTPase binding RABEP1 effector protein 1 405 B.225834_at AL135396 Hs.339665 Similar to RIKEN cDNA MGC57827 2700049P18 gene 406 A.205591_at NM_006334 Hs.74376 Olfactomedin 1 OLFM1 407 B.228058_at AI559190 Hs.105887 Similar to common salivary LOC124220 protein 1 408 A.207828_s_at NM_005196 Data not found 409 A.222379_at AI002715 Hs.348522 Potassium voltage-gated KCNE4 channel, Isk-related family, member 4 410 A.210084_x_at AF206665 Hs.405479 Tryptase, alpha TPS1 411 B.233249_at AU155297 Hs.287562 CDNA FLJ13313 fis, clone OVARC1001489 412 B.232948_at AU147218 Hs.297369 CDNA FLJ12111 fis, clone MAMMA1000025 413 B.229033_s_at AA143060 Hs.454758 Melanoma associated antigen MUM1 (mutated) 1 414 B.229623_at BF508344 Hs.112742 CDNA clone IMAGE: 6301163, containing Frame-shift errors 415 A.222339_x_at AI054381 Hs.293379 Transcribed sequences 416 A.205347_s_at NM_021992 Hs.56145 Thymosin, beta, identified in TMSNB neuroblastoma Cells 417 B.229245_at AA535361 Hs.343666 Phosphoinositol 3-phosphate- PEPP3 binding Protein-3 418 B.225491_at AL157452 Hs.349088 Solute carrier family 1 (glial SLC1A2 high affinity Glutamate transporter), member 2 419 B.239594_at BF110735 Data not found 420 A.213906_at AW592266 Hs.300592 v-myb myeloblastosis viral MYBL1 oncogene homolog (avian)-like 1 421 B.223757_at AF305836 Hs.406958 Deiodinase, iodothyronine, type DIO3OS III opposite Strand 422 B.242296_x_at BF594828 Hs.91145 Transcribed sequences 423 B.236312_at AA938184 Hs.44380 Transcribed sequence with weak similarity to protein ref: NP_071385.1 (H. sapiens) hypothetical protein FLJ20958 [Homo sapiens] 424 B.227529_s_at BF511276 Hs.197081 A kinase (PRKA) anchor protein AKAP12 (gravin) 12 425 A.221928_at AI057637 Hs.234898 acetyl-Coenzyme A carboxylase ACACB beta 426 B.244013_at AI084430 Hs.113919 Hypothetical protein LOC374969 LOC374969 427 A.219769_at NM_020238 Hs.142179 inner centromere protein INCENP antigens 135/155 kDa 428 B.239758_at AI142126 Hs.26125 Transcribed sequences 429 B.239913_at AI421796 Hs.132591 solute carrier family 10 SLC10A4 (sodium/bile acid cotransporter family), member 4 430 A.211226_at AF080586 Hs.158351 galanin receptor 2 GALR2 431 A.206023_at NM_006681 Hs.418367 Neuromedin U NMU 432 A.210538_s_at U37546 Data not found 433 B.232277_at AA643687 Hs.149425 solute carrier family 28 (sodium- SLC28A3 coupled nucleoside transporter), member 3 434 A.207339_s_at NM_002341 Hs.376208 Lymphotoxin beta (TNF LTB superfamily, member 3) 435 A.37145_at M85276 Data not found 436 B.243837_x_at AA639707 Hs.443239 Transcribed sequences 437 A.221198_at NM_021920 Data not found 438 B.233442_at AU147500 Hs.287499 CDNA FLJ12196 fis, clone MAMMA1000867 439 B.232545_at AF176701 Hs.442734 F-box and leucine-rich repeat FBXL9 protein 9 440 B.238323_at BG387172 Hs.528776 TEA domain family member 2 TEAD2 441 B.231993_at AK026784 Hs.301296 CDNA: FLJ23131 fis, clone LNG08502 442 B.224212_s_at AF169689 Hs.247734 Protocadherin alpha 2 PCDHA2 443 B.231560_at D59759 Data not found 444 A.201195_s_at AB018009 Hs.184601 solute carrier family 7 (cationic SLC7A5 amino acid transporter, y+ system), member 5 445 B.239185_at AI284184 Hs.388917 ATP-binding cassette, sub- ABCA9 family A (ABC1), member 9 446 B.232776_at AU145289 Hs.193223 CDNA FLJ11646 fis, clone HEMBA1004394 447 A.212865_s_at BF449063 Hs.512555 collagen, type XIV, alpha 1 COL14A1 (undulin) 448 B.228750_at AI693516 Hs.28625 Transcribed sequences 449 B.241577_at AI732794 Data not found 450 A.209125_at J00269 Data not found 451 B.238898_at BG028463 Hs.163734 Transcribed sequences 452 A.203548_s_at BF672975 Hs.180878 lipoprotein lipase LPL 453 B.230363_s_at BE858808 Hs.52463 inositol polyphosphate-5- INPP5F phosphatase F 454 A.221111_at NM_018402 Hs.272350 interleukin 26 IL26 455 B.226597_at AI348159 Hs.76277 polyposis locus protein 1-like 1 DP1L1 456 A.218169_at NM_018052 Hs.445061 Hypothetical protein FLJ10305 FLJ10305 457 A.206107_at NM_003834 Hs.65756 regulator of G-protein signalling RGS11 11 458 B.230158_at AA758751 Hs.484250 Hypothetical protein FLJ32949 FLJ32949 459 B.244706_at AA521309 Hs.380763 similar to hypothetical protein LOC115294 FLJ10883 460 B.228648_at AA622495 Hs.10844 leucine-rich alpha-2- LRG1 glycoprotein 1 461 B.237047_at AI678049 Hs.508819 CDNA FLJ40458 fis, clone TESTI2041778 462 A.205671_s_at NM_002120 Hs.1802 major histocompatibility HLA-DOB complex, class II, DO beta 463 A.217167_x_at AJ252550 Data not found 464 A.205399_at NM_004734 Hs.21355 Doublecortin and CaM kinase- DCAMKL1 like 1 465 B.236646_at BE301029 Hs.226422 Hypothetical protein FLJ31166 FLJ31166 466 A.203354_s_at AW117368 Hs.408177 ADP-ribosylation factor guanine EFA6R nucleotide factor 6 467 B.237252_at AW119113 Hs.2030 Thrombomodulin THBD 468 A.206341_at NM_000417 Hs.130058 interleukin 2 receptor, alpha IL2RA 469 A.210525_x_at BC001787 Hs.123232 Chromosome 14 open reading C14orf143 frame 143 470 A.214897_at AB007975 Hs.492779 MRNA, chromosome 1 specific transcript KIAA0506. 471 A.203362_s_at NM_002358 Hs.79078 MAD2 mitotic arrest deficient- MAD2L1 like 1 (yeast) 472 B.230874_at AI241896 Hs.48653 CDNA FLJ39593 fis, clone SKNSH2001222 473 B.224396_s_at AF316824 Hs.435655 asporin (LRR class 1) ASPN 474 A.208305_at NM_000926 Hs.2905 Progesterone receptor PGR 475 B.223867_at AF334676 Hs.414648 tektin 3 TEKT3 476 A.211363_s_at AF109294 Hs.459541 Methylthioadenosine MTAP phosphorylase 477 B.232267_at AL162032 Hs.23644 G protein-coupled receptor 133 GPR133 478 B.244121_at BE835502 Data not found 479 B.242808_at AI733287 Hs.203755 Transcribed sequence with moderate similarity to protein sp: P12947 (H. sapiens) RL31_HUMAN 60S ribosomal protein L31 480 A.215465_at AL080207 Hs.134585 ATP-binding cassette, sub- ABCA12 family A (ABC1), member 12 481 A.210244_at U19970 Hs.51120 Cathelicidin antimicrobial CAMP peptide 482 A.204603_at NM_003686 Hs.47504 Exonuclease 1 EXO1 483 B.232986_at AC074331 Data not found 484 B.225241_at BG253437 Hs.356289 steroid sensitive gene 1 URB 485 B.230760_at BF592062 Hs.169859 zinc finger protein, Y-linked ZFY 486 A.209480_at M16276 Hs.409934 major histocompatibility HLA-DQB1 complex, class II, DQ beta 1 487 A.206664_at NM_001041 Hs.429596 Sucrase-isomaltase (alpha- SI glucosidase) 488 A.206291_at NM_006183 Hs.80962 Neurotensin NTS 489 A.222085_at AW452357 Hs.27373 Hypothetical gene supported by LOC400451 AK075564; BC060873 490 A.214899_at AC007842 Data not found 491 B.240174_at BF512871 Hs.193522 Transcribed sequence with moderate Similarity to protein sp: P39188 (H. sapiens) ALU1_HUMAN Alu subfamily J sequence Contamination warning entry 492 A.219148_at NM_018492 Hs.104741 T-LAK cell-originated protein TOPK kinase 493 B.226303_at AA706788 Hs.46531 Phosphoglucomutase 5 PGM5 494 B.222848_at BC005400 Hs.164018 Leucine zipper protein FKSG14 FKSG14 495 A.202270_at NM_002053 Hs.62661 Guanylate binding protein 1, GBP1 interferon-inducible, 67 kDa 496 A.205266_at NM_002309 Hs.2250 leukemia inhibitory factor LIF (cholinergic differentiation factor) 497 B.239008_at AW606588 Hs.430335 Transcribed sequence with weak similarity to protein sp: P39195 (H. sapiens) ALU8_HUMAN Alu subfamily SX sequence contamination warning entry 498 B.228194_s_at AI675836 Hs.348923 sortilin-related VPS10 domain SORCS1 containing receptor 1 499 A.215514_at AL080072 Hs.21195 MRNA; cDNA DKFZp564M0616 (from clone DKFZp564M0616) 500 A.219010_at NM_018265 Hs.73239 Hypothetical protein FLJ10901 FLJ10901 The 500-gene classifier: The genes are ranked according to their correlation with p53 status. The genes are identified by their GenBank Accession Nos., Affymetrix Probeset IDs, Unigene IDs, Unigene Names and Unigene Symbols.

For sequences and SEQ ID NOs for the genes described in Table 1, see FIGS. 9-508 in which each of the sequences for the above genes is shown and is associated with a GenBank Accession No., Unigene ID, and/or a Unigene Name, and a SEQ ID NO.

Example 3 The p53 Classifier has Significant Accuracy in Two Independent Datasets

The performance of the p53 classifier in the context of independent datasets was then evaluated. FIG. 3 shows that genes of the classifier can predict p53 status in independent cDNA microarray datasets. (A) A 9-gene subset of the 32-gene classifier can predict p53 status in an independent breast cancer dataset. 9 genes of our classifier were selected based on their presence in 50% or more of the tumors. The tumors used in the analysis were required to have expression data present for >50% of the genes. (B) An 8-gene subset of the p53 classifier can predict p53 status in an independent liver cancer dataset. 8 overlapping genes were selected based on their presence in 90% or more of the tumors. The tumors used in the analysis were required to have expression data present for >50% of the genes. (A&B) Black vertical bars indicate p53 mutant status. Gene symbols (Unigene build #167) and corresponding IMAGE clone IDs (from the original studies) are listed. The hierarchical clustergrams are shown. Genes (rows) and tumors (columns) were clustered. In the tumor dendrograms, the green branch denotes the wildtype-like configurations, and the red branch the mutant-like profiles.

Two publicly available microarray datasets where p53 status was known, were therefore accessed: a breast cancer study by Sorlie et al (Sorlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100, 8418-23 (2003), incorporated herein by reference) and a liver cancer study by Chen et al (Chen, X. et al. Gene expression patterns in human liver cancers. Mol Biol Cell 13, 1929-39 (2002), incorporated herein by reference). Both studies were conducted on cDNA microarray platforms.

In the Sorlie dataset, 69 breast tumors were sequenced for p53 mutations. This subset of tumors was queried for the availability of expression data corresponding to the genes of the classifier. Twenty-eight genes in the classifier mapped to UniGene IDs (build #167). Though over half of these genes mapped to the Sorlie et. al. microarray, few were expressed in the majority of the tumors, and a number of tumors possessed measurements for less than half of the genes. Only 9 genes in the classifier were found to correspond to cDNA probes (representing 9 different genes) having expression measurements present in >50% of the tumors, where the tumors possessed measurements for >50% of the genes (resulting in a subset of 44 well-sampled tumors). Using this 9-gene subset of the classifier to hierarchically cluster the tumors (FIG. 3A), 77% of the p53 mt tumors clustered into one branch, and 77% of the wildtypes clustered into the other (p_(cs)=3.0×10⁻⁴) recapitulating the robust predictive capability of the classifier.

A cDNA-microarray based liver cancer dataset where p53 status was ascertained by immunohistochemistry, IHC (Chen, X. et al. Gene expression patterns in human liver cancers. Mol Biol Cell 13, 1929-39 (2002), incorporated herein by reference) was next analyzed. In this study, p53 protein levels were ascertained by IHC. Here, 8 classifier genes could be mapped to all 59 tumors assayed for p53 status (with each gene having data present in 90% or more of all tumors, and where each tumor contained data for >50% of the genes). With similar statistical significance as that seen in the breast cancer dataset (i.e, p_(fe)=3.5×10⁻⁴), this 8-gene subset of the classifier was able to cluster the HCC samples into two predominant clusters correlated with p53 status: 87% of the mutants in one cluster, and 61% of the wildtypes in the other (FIG. 3B). Together, these observations suggest that the genes comprising the p53 classifier are robust in their ability to classify not only breast tumors based on p53 status, but also liver cancers, and therefore may have generalizable utility in predicting p53 status in other cancer types.

TABLE 2 Genbank Affymetrix UniGene ID UniGene Accession No. Probeset ID (build #171) UniGene Name (build #167) Symbol AI961235 B.235343_at Hs.96885 Hypothetical protein FLJ12505 FLJ12505 BG271923 B.238581_at Hs.237809 Guanylate binding protein 5 GBP5 NM_002466 A.201710_at Hs.179718 v-myb myeloblastosis viral MYBL2 oncogene homolog (avian)-like 2 BC001651 A.221520_s_at Hs.48855 Cell division cycle associated 8 CDCA8 D38553 A.212949_at Hs.308045 Barren homolog (Drosophila) BRRN1 AK000345 A.214079_at Hs.272499 Dehydrogenase/reductase (SDR DHRS2 family) member 2 AA742697 B.230378_at Hs.62492 Secretoglobin, family 3A, member 1 SCGB3A1 AL080170 A.215047_at BF245284 B.238746_at Hs.354427 Transcribed sequences BC004504 A.221585_at Hs.331904 Calcium channel, voltage- CACNG4 dependent, gamma subunit 4 H15261 B.243929_at Hs.21948 Transcribed sequences NM_000909 A.205440_s_at Hs.519057 Neuropeptide Y receptor Y1 NPY1R NM_024843 A.217889_s_at Hs.31297 Cytochrome b reductase 1 CYBRD1 R73030 B.230863_at Hs.252938 Low density lipoprotein-related LRP2 protein 2 NM_030896 A.221275_s_at AI435828 A.203438_at Hs.155223 Stanniocalcin 2 STC2 AL512727 A.215014_at Hs.232127 MRNA; cDNA DKFZp547P042 (from clone DKFZp547P042) AW242997 B.229030_at AI810764 B.229150_at Hs.102406 Transcribed sequences AI922323 B.228969_at Hs.226391 Anterior gradient 2 homolog AGR2 (Xenopus laevis) AL360204 B.232855_at Hs.283853 MRNA full length insert cDNA clone EUROIMAGE 980547 NM_003225 A.205009_at Hs.350470 Trefoil factor 1 (breast cancer, TFF1 estrogen-inducible sequence expressed in) NM_003226 A.204623_at Hs.82961 Trefoil factor 3 (intestinal) TFF3 AW299538 B.227081_at Hs.75528 Nucleolar GTPase HUMAUAN TIG NM_003462 A.205186_at Hs.406050 Dynein, axonemal, light DNALI1 intermediate polypeptide 1 AI990465 A.205734_s_at Hs.38070 Lymphoid nuclear protein related LAF4 to AF4 NM_004392 A.205472_s_at Hs.63931 Dachshund homolog (Drosophila) DACH1 NM_001267 A.206869_at Hs.97220 Chondroadherin CHAD AF269087 B.223864_at Hs.326736 Breast cancer antigen NY-BR-1 NY-BR-1 AI826437 B.229975_at Hs.283417 Transcribed sequences AL355392 B.226067_at AU156421 B.233413_at Hs.518736 CDNA FLJ13457 fis, clone PLACE1003343. Optimized 32-gene p53 Classifier: The genes are identified by their GenBank Accession Nos., Affymetrix Probeset IDs, Unigene IDs, Unigene Names and Unigene Symbols.

Example 4 The p53 Classifier is a Greater Prognostic Indicator of Patient Outcome than p53 Mutation Status Alone

It is widely accepted that in breast cancer and other tumor types p53 status is prognostic of clinical outcomes such as tumor recurrence, patient survival, and therapeutic response. The hypothesis that a classifier based on p53 activity would out-perform p53 mutation status alone as a prognostic indicator of clinical outcomes was tested. FIG. 4 shows that the p53 classifier has greater prognostic significance than p53 mutation status alone. Kaplan-Meier survival curves are shown for patients classified according to (A) p53 mutation status, (B&C) the p53 classifier, or (D) both. The clinical endpoint was death from breast cancer (ie, disease-specific survival). In A,B, and D all 257 patients were assessed; in C, only the 198 patients with p53 wildtype tumors were assessed. The Wald test (p_(w)) was used to assess significance of the hazard ratios (HR).

The classifier and sequence-level p53 mutation status were compared with respect to their abilities to predict disease-specific survival (DSS) in all 257 patients of the Uppsala cohort regardless of treatment type or clinical stage.

The significance of the hazard ratio generated using the p53 classifier to segregate patients was an order of magnitude greater than that obtained using p53 mutation status alone (p_(w)=0.00057 versus p_(w)=0.012, respectively) (FIG. 4 A&B); notably, this improved p-value was statistically significant at p_(mc)=0.0046. Furthermore, the p53 classifier could also significantly segregate patients into low and high risk groups in the subset of 198 women confirmed by sequencing to have wildtype p53 (p_(w)=0.016) (FIG. 4C) indicating that those with p53 wt tumors classified as mutant-like have poorer DSS than those with wt tumors of the wt-like class. In FIG. 4D, survival curves among all four tumor subgroups were compared. Notably, it was observed that patients with p53 mt or wt tumors classified as mt-like (green and blue curves, respectively) have similar overall survival curves, while the twelve with p53 mt tumors classified as wt-like (red curve) show a survival curve that falls between that of the group with mutant-like p53 mt tumors (green curve) and that of the group with wt-like p53 wt tumors (black curve) and is not significantly different from either curve (p_(w)=0.47 for mt/mt-like comparison and p_(w)=0.37 for wt/wt-like comparison).

Next, the prognostic significance of the classifier on the Sorlie et al cDNA microarray dataset was examined (Sorlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100, 8418-23 (2003), incorporated herein by reference). FIG. 5 shows that the p53 classifier has strong prognostic significance in an independent dataset of late-stage tumors. Tumors were hierarchically classified according to the 9-gene partial classifier described in FIG. 3 and analyzed for correlations with survival outcomes: (A) hierarchical clustergram of 76 tumors from the Sorlie et al dataset; the black branch of the tumor dendrogram denotes the wildtype-like configuration, and the red branch the mutant-like profile. Shown are Kaplan-Meier estimates for (B) disease-specificsurvival and (C) disease-free survival, where patient groups were determined according to the green and red branches of the tumor dendrogram in (A).

Here, the 9-gene partial classifier that could distinguish mt and wt tumors both with 77% accuracy, was used to hierarchically cluster 76 well-sampled tumor specimens with associated patient survival information (FIG. 5A). Importantly, the majority of these tumors (>80%) are derived from two independent prospective studies on chemotherapeutic response of stage III patients with locally advanced breast cancer (T3/T4 and/or N2). The tumors clustered into two predominant branches with 31 tumors in the wt-like cluster and 44 tumors in the mutant-like cluster. Grouping the patients according to these tumor profiles, the Kaplan-Meier survival curves for disease-specific and disease-free survival (FIGS. 5B& C) were both highly significant in this cohort (p_(w)=0.00008 (DSS) and p_(w)=0.00005 (DFS)). Remarkably, the 31 patients in the p53 wt-like cluster showed a 90% probability of surviving their breast cancer for a period of 7 years compared to a 35% probability of 7-year survival for the 44 patients in the p53 mt-like group (FIG. 5B). Thus, in this predominantly stage III patient population, the partial classifier can accurately predict not only which patients will relapse and die, but also which late stage patients will survive their cancer.

For hierarchical cluster analysis, log expression values were mean centered and normalized, and genes and tumors were clustered using the Pearson correlation metric and average linkage (Cluster and TreeView software courtesy Dr. Michael Eisen; software available on Lawrence Berkeley National Laboratory, UC Berkeley's website). For survival analysis, patients were stratified according to the p53 classifier output or, as in one case, according to p53 mutation status. The Kaplan Meier estimate was used to compute survival curves for the different patient groups and the Wald Test was used to assess the statistical significance of the resultant hazard ratio. The FIG. 4 survival analysis assesses the probability of achieving, by chance alone, the more significant Wald p-value of 0.00057 generated using the group assignments as determined by the p53 classifier (panel B) compared to p=0.012 using p53 status alone (panel A). In 100,000 iterative runs, 40 tumors were randomly selected (ie, the number of tumors that differed in group assignment between panel A and B), their p53 status inverted, and the Wald p-values computed for each run. A p-value≦0.00057 was obtained only 564 times. The Monte Carlo p-value for this observation is estimated to be 0.0046.

For association tests (i.e., to ascertain the significance of the number of observed events in two or more groups), the Chi-square test was employed. When the number of events was sufficiently small (<5) in any category, Fisher's Exact test was applied instead of Chi-square test.

For the statistical analysis of expression levels for p53 downstream target genes and upstream effectors, two-tailed two-group T tests were employed to determine differentially expressed genes between the p53 wt and mt tumors (FIG. 8). One-tailed two group t-tests were performed for comparisons between the p53 wt tumors in the mt-like class and the p53 wt tumors in the wt-like class (and vice versa) to test whether the genes were significantly differentially expressed in the same direction (or opposite direction) as that observed between the p53 wildtypes and mutants.

It would be evident to one of skill in the art that the method embodiments of the present invention are not limited to the statistical methods disclosed herein. Embodiments of the present invention encompass equivalent analytical methods. The p-value abbreviations used herein include:

p_(wr)=Wilcoxon rank-sum test

p_(t)=T test

P_(cs)=Chi-square test

p_(fe)=Fisher's Exact test

p_(w)=Wald test P_(mc)=Monte Carlo estimate

Promoter analysis for p53 binding sites was performed on each of the classifier genes with a known transcription start site (TSS). BEARR (Vega, V. B., Bangarusamy, D. K., Miller, L. D., Liu, E. T. & Lin, C. Y. BEARR: Batch Extraction and Analysis of cis-Regulatory Regions. Nucleic Acids Res 32, W257-60 (2004), incorporated herein by reference) was used to extract promoter sequences (3000 bp upstream to 500 bp downstream of the TSS) and predict putative binding sites using the P53 position weight matrix obtained from TRANSFAC (Kel, A. E. et al. MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res 31, 3576-9 (2003), incorporated herein by reference) version 6.0 (Matrix accession: M00272) as well as simple pattern search based on the canonical p53 binding site consensus 5′-RRRCWWGYYYN(0-13)RRRCWWGYYY-3′ (el-Deiry, W. S., Kern, S. E., Pietenpol, J. A., Kinzler, K. W. & Vogelstein, B. Definition of a consensus binding site for p53. Nat Genet 1, 45-9 (1992), incorporated herein by reference.

Example 5 The p53-Deficiency Classifier, but not p53 Status Alone, is Significantly Correlated with Outcome in Endocrine-Treated Patients.

To further test the robustness of the classifier in predicting patient outcome, its performance in other relevant therapeutic treatment groups was analyzed. Recently, it has been observed that p53 mt breast tumors show greater resistance to endocrine therapy than p53 wt tumors, and this has been explained, in part, by the uncoupling of p53-dependent apoptosis in the resistant tumors (Berns, E. M. et al. Complete sequencing of TP53 predicts poor response to systemic therapy of advanced breast cancer. Cancer Res 60, 2155-62 (2000), incorporated herein by reference). To test the ability of the classifier to predict outcome in a hormone therapy-specific patient cohort, a subpopulation of the Uppsala cohort consisting of 68 ER+ patients who received only adjuvant tamoxifen treatment following surgery, was examined. FIG. 6 shows that the p53 classifier has greater prognostic significance than p53 mutation status in endocrine-treated patients. Sixty-eight ER+, endocrine-treated patients were classified according to (A) p53 mutation status or (B) the p53 classifier and analyzed for correlations with disease-specific survival (DSS). Kaplan-Meier survival estimates are shown. As shown in the survival analysis in FIG. 6A&B, it was observed that the classifier was a significant predictor of disease-specific survival (p_(w)=0.047), while p53 mutation status alone was not (p_(w)=0.395).

Next, the prognostic performance of the classifier on a set of 97 breast tumors published by van't Veer et al (van't Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-6 (2002), incorporated herein by reference) was examined. FIG. 7 shows that the p53 classifier is prognostic of distant recurrence in an independent set of early-stage locally-treated breast tumors. 97 tumors from a Dutch cohort (van't Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-6 (2002), incorporated herein by reference) of early-stage patients treated with postoperative adjuvant radiotherapy and followed for a period of at least 5 years were hierarchically clustered using a set of probes corresponding to 21 genes of the optimized classifier. The predominant cluster nodes are demarcated by color and “C” designations (i.e., C1-C5). Black arrows correspond to tumors from patients who developed a distant metastasis (DM) within 5 years. Gene symbols and corresponding Genbank accession numbers are shown. Hierarchical clustering was performed as described previously.

Here, all of the samples were controlled for clinical uniformity, i.e., <5 cm in size (T1/T2), with no advanced disease (pN0), from patients less than 55 years of age at diagnosis, treated by surgery and subsequent radiotherapy only (with the exception of 5 patients who received adjuvant systemic therapy). From the 32-gene classifier, 24 probes corresponding to 21 genes could be mapped to all 97 tumors with survival information. Upon clustering the tumors, approximately 4 clusters with similar average distance correlations were observed that significantly distinguished patients who would develop a distant metastasis within 5 years (p_(fe)=2.2×10⁻⁴) (FIG. 7). Notably, of the 26 tumors in cluster 1, which bear the molecular configuration of p53 mt-like tumors, 73% had a distant metastasis within 5 years, compared to 26% of 39 tumors in cluster 3, which most closely resemble the p53 wt-like molecular configuration. These findings suggest that the p53 classifier is prognostic of tumor recurrence in early stage, locally-treated breast cancer.

Example 6 Analysis of Classifier Gene Functions

To gain some mechanistic insights, the functional annotations of the classifier genes were analysed for clues to explain the correlation between their expression levels and p53 status and patient outcome. Surprisingly, it was found that none of the classifier genes are known transcriptional targets of p53, nor have they been previously implicated in the p53 pathway. Promoter analysis of the 21 genes with defined promoter regions revealed no evidence of the canonical p53 binding site, or recently described novel p53 binding sites, within any of the promoters.

Twelve of the genes are of unknown function. However, of the characterized genes, a number are associated with cell growth and proliferation (MYBL2, TFF1, BRRN1, CHAD, SCGB3A1, DACH, CDCA8), transcription (LAF4, NY-BR-1, DACH, MYBL2), ion transport (CACNG4, CYBRD1, LRP2), and breast cancer biology (SCGB3A1, TFF1, STC2, NY-BR-1, AGR2). Speculatively, some of these genes may contribute mechanistically to the poor prognosis of the p53 mutant-like tumors. For example, MYBL2, which was observed to be upregulated in the p53 mutant-like tumors, is a growth-promoting transcription factor closely related to the c-MYB oncogene. It maps to a chromosomal region frequently amplified in breast cancer (20q13) and has previously been reported to be overexpressed in breast cancer cell lines and sporadic ovarian carcinomas (Forozan, F. et al. Comparative genomic hybridization analysis of 38 breast cancer cell lines: a basis for interpreting complementary DNA microarray data. Cancer Res 60, 4519-25 (2000) and Tanner, M. M. et al. Frequent amplification of chromosomal region 20q12-q13 in ovarian cancer. Clin Cancer Res 6, 1833-9 (2000), both of which are incorporated herein by reference. SCGB3A1 (HIN1), which was observed to be downregulated in the p53 mutant-like tumors, is a putative tumor suppressor gene that can inhibit breast cancer cell growth when overexpressed and has been found to be transcriptionally silenced by hypermethylation of its promoter in early stages of breast tumorigenesis (Krop, I. E. et al. H1N-1, a putative cytokine highly expressed in normal but not cancerous mammary epithelial cells. Proc Natl Acad Sci U S A 98, 9796-801 (2001), incorporated herein by reference).

Example 7 Nature of Misclassified Tumors

It was observed that a number of cancers with wild type p53 sequence status were classified as p53 mutant by expression profiling using the 32-gene classifier. If the “misclassified” p53 wt tumors were in fact p53 deficient, they would possess certain molecular characteristics reflective of perturbations of the p53 pathway, and these characteristics would be found in the majority of p53 mutant tumors. First, the possibility that p53 deficiency could result from reduced transcript levels either by transcriptional repression of the p53 gene (TP53) or by the shortening of its mRNA half-life, was considered. The t test was used to compare the relative expression levels of TP53 (using the TP53 probe-sets present on the microarray) among the different tumor classes (FIG. 8). Indeed, consistent with this hypothesis, it was observed that the overall expression level of TP53 was significantly reduced in the 28 wt tumors classified as mt-like compared to the remaining 170 wt tumors classified as wt-like (p_(t)=1.4×10⁻⁰⁴). No statistically significant difference in expression levels was observed between the p53 mt tumors correctly classified as mt-like and all wt tumors, consistent with the fact that TP53 mRNA levels are not commonly reduced in p53 mutant breast cancers.

FIG. 8 shows that transcript levels of p53, its transcriptional targets, and its upstream effectors distinguish known and predicted classes. Expression levels of p53 pathway-relevant genes were examined. The statistical significance of transcript levels between the different tumor classes was determined by t test and is shown in a summary table to the right of the figure. The 4 tumor classes are as follows: 1) 47 p53 mt tumors classified as mutant, 2) 28 p53 wt tumors classified as mutant, 3) 170 p53 wt tumors classified as wildtype, and 4) 12 p53 mt tumors classified as wildtype. Statistical measurements in the summary shown in grey did not reach significance at p<0.05.

Table 3 shows a comparative analysis of p53 mutations. (I) Severe mutations were defined as insertions, deletions, or stop codons. Of the remaining missense point mutations (mpms; 11 in the wt-like group, 27 in the mt-like group) we determined the frequency of occurrence of (II) the most common missense point mutations in p53 as defined by the IARC TP53 Mutation Database (available online on the website of the International Agency for Research on Cancer, IARC), and (III) mutants previously shown, in vitro, to possess dominant negative activity were determined. P-values were calculated using Fisher's Exact test.

This strategy was applied to known transcriptional targets of p53, which were hypothesized to show altered transcription in p53-deficient tumors to some extent. Indeed, a number of p53 target genes demonstrated altered patterns of expression (FIG. 8). The TP53-inducible genes TP53INP1, SEMA3B, PMAIP1 (NOXA), FDXR, CCNG1, and LRDD, all of which contain functional p53-binding sites in their promoters, showed significantly lower expression in the 28 wt tumors classified as mt-like compared to the other wildtypes (all at p_(t)<0.05). Moreover, all but one of these genes were also significantly reduced in the p53 mt tumors classified as mt-like (compared to all wt tumors); and in all but two cases, these genes showed significantly higher expression in the 12 mt tumors classified as wt-like when compared to the other mutants.

CHEK1 and CHEK2, both positive upstream effectors of p53 that phosphorylate p53 and thereby promote its stabilization, are known to be transcriptionally repressed by p53. A significant increase in the mRNA levels of these genes in both the p53 wt and mt tumors of the mutant-like class was observed. It was also observed that the 12 mt tumors misclassified as wildtype-like displayed significantly lower expression of these genes compared to the other 47 p53 mutants. Notably, no differential expression of the p53-regulated genes CDKN1A (p21), GADD45, PPM1D (WIP1), TP53I3 (PIG3), TNFRSF6, BBC3 (PUMA), APAF1 or BCL2 was observed in these breast tumor specimens.

Taken together, these data suggest that the classifier can distinguish tumors based on some aspects of p53 transcriptional activity that are inhibited in both the p53 mutant and wildtype tumors of the mutant-like class, yet operative in the p53 wildtype tumors (and to some extent the 12 p53 mutant tumors) of the wildtype-like class.

Perhaps paradoxically, it was observed that the p53-inducible genes PERP, BAX and SFN (14-3-3 sigma) were all expressed at significantly higher levels in the 28 misclassified wt tumors, rather than at lower levels like their inducible gene counterparts described above. However, the significant overexpression of these genes in the p53 mt tumors classified as mutant-like was also observed, suggesting that in breast cancer, these genes may be induced by alternate regulatory mechanisms in the context of mutant or deficient p53.

Intriguingly, another positive upstream effector of p53, ATR, which is thought to enhance p53 activity in a manner similar to that of CHEK1 and CHEK2, was also found expressed at significantly higher levels in the p53 mutants and p53 wt tumors of the mutant-like class, even though this gene is not known to be modulated in a p53-dependent manner. Of note, no significant differences in the expression levels of the upstream effectors, ATM or PRKDC (DNA-PK) were observed.

The expression levels of other upstream modulators of p53 activity were then examined in order to ascertain possible alternate mechanisms by which p53 expression and activity might be reduced in the mutant-like p53 wt tumors. First, it was observed that several known positive regulators of p53 transactivation were significantly reduced in both the wildtypes and mutants of the mutant-like class including HOXA5, USF1, EGR1 and TP53BP1. HOXA5, USF1, and EGR1 are all transcription factors known to bind the p53 promoter and enhance its expression. Interestingly, deficiencies in all three have previously been implicated in breast carcinogenesis. Recently the coordinate loss of both p53 and HOXA5 mRNA and protein expression was observed in a panel of human breast cancer cell lines, and the HOXA5 promoter was found to be methylated in 16 of 20 p53-negative human breast tumors. USF1, which is structurally related to the c-Myc oncoprotein, has been found to have reduced transcriptional activity in breast cancer cell lines, and has recently been shown to activate the expression of estrogen receptor alpha. EGR1, a DNA damage-responsive gene with antiproliferative and apoptotic functions, can inhibit tumorigenicity when exogenously expressed in human breast cancer cells, and has been observed to have reduced expression in human and mouse breast cancer cell lines and tumors. TP53BP1 is not thought to be a transcription factor, but rather a BRCT domain-containing substrate of ATM that is phosphorylated in response to DNA damage. This gene product is known to bind the central DNA-binding domain of p53 and thus enhance the transcriptional activation of p53 target genes. A significantly reduced expression of all four genes in the 28 p53 wt tumors classified as mutant-like was found, and in the cases of USF1 and TP53BP1, significantly higher expression in the p53 mutants classified as wildtype-like. Interestingly, it was also observed that their expression levels are also significantly lower in the 47 p53 mt tumors classified as mutant-like, suggesting a possible positive feedback loop whereby wildtype p53 can enhance expression of these genes and impaired p53 cannot. Together, these observations suggest the possibility that either acting separately or in combination, these genes may be important for intact p53 activity in the breast, and when transcriptionally silenced, contribute to p53 deficiency.

Finally, the expression of several known negative regulators of p53 activity were examined. Notably, MDM2, which negatively regulates p53 through phosphorylation-mediated degradation of the p53 protein, and whose overexpression at the protein level has been implicated in a variety of cancers, was not found to be differentially expressed at the transcript level in the experiments described herein. However, both PLK1 and GTSE1 were. The M-phase regulator PLK1 has recently been shown to bind to the DNA-binding domain of p53 and thus inhibit its transcriptional activity in vitro. GTSE1 (B99) binds the C-terminal regulatory domain of p53 causing the inhibition of p53 transactivation function as well as a reduction of intracellular levels of p53 protein. Intriguingly, the transcript levels of both genes were among the most highly significantly overexpressed in both p53 wt and mt tumors of the mt-like class, suggesting a possible role for these gene products in suppression of p53 function in breast carcinogenesis.

The spectrum of p53 mutations for correlations that might explain the misclassification of the 12 p53-mutant tumors as wildtype-like was next analyzed. First, it was observed that only one mutation was common to the wildtype-like and the mutant-like tumors: a Tyr>Cys at amino acid 220 in the DNA-binding domain. Of the 47 p53 mt tumors correctly classified as mutants, it was observed that 42% (20/47) possessed “severe” mutations defined as insertions (n=2), deletions (n=11) and stop codons (n=7) (Table 3-I) resulting in frameshifts and subsequent trunctation, whereas in the 12 mutants classified as wildtype-like, only 1 (8%) contained a severe mutation: a 3-bp insertion in the DNA-binding domain resulting in the inframe addition of a glycine residue (p_(fe)=0.025). Using the IARC TP53 Mutation Database (available online on the website of the International Agency for Research on Cancer, IARC), which, as of June 2003, has indexed 18,585 somatic and 225 germline mutations of p53, the frequencies of occurrence of the most common p53 mutations in human cancer (representing ˜20% of all p53 mutations; Table 1-II) in the 12 wt-like mutants and the 47 mt-like mutants were compared. None of the common mutations were found to overlap with the subset of 11 missense point mutations (mpms) in the wt-like group, compared to 9 of 27 in the mt-like group (p_(fe)=0.029). The mpms in each tumor group was then cross-compared with the IARC TP53 Mutation Database's comprehensive listing of 418 mutants previously analyzed for dominant negative function in at least one of 44 previously published studies. As Table 2-III shows, it was found that only one of the 11 mpms among the 12 wt-like mutants had been demonstrated previously to have dominant negative activity, compared to 12 of 27 within the mt-like group (p_(fe)=0.039). Together, these data suggest that at the sequence level, the 12 p53 mutants classified as wildtype-like may in fact comprise of mostly “benign” p53 mutant forms compared to those 47 classified as mutant-like, in agreement with their molecular consistencies with the majority of p53 wt tumors in our expression analyses.

TABLE 3 Comparative analysis of p53 mutations. 12 wt-like 47 mt-like mutation type tumors tumors p-value: I. severe mutations: 1 20 0.025 deletions 0 11 stop codons 0 7 insertions 1 2 (11 tumors (27 tumors with mpms) with mpms) II. Common missense 0 9 0.029 pt. mutations: 175 (Arg->His) 0 2 248 (Arg->Gln) 0 3 248 (Arg->Trp) 0 2 273 (Arg->His) 0 0 273 (Arg->Cys) 0 2 282 (Arg->Trp) 0 0 III. pt. mutations with 1 12 0.039 known dominant negative function: (I) Severe mutations were defined as insertions, deletions, or stop codons. Of the remaining missense point mutations (mpms; 11 in the wt-like group, 27 in the mt-like group) we determined the frequency of occurrence of (II) the most common missense point mutations in p53 as defined by the IARC TP53 Mutation Database (http://www.iarc.fr/p53/index.html), and (III) mutants previously shown, in vitro, to possess dominant negative activity. P-values were calculated using Fisher's Exact test.

The practice of the present invention may employ conventional biology methods known to the skilled artisan, software and systems. The foregoing examples have described methods for predicting disease outcome in a patient. In another aspect, there is also provided a computer system for predicting disease outcome in a patient. The computer system may comprise a computer having a processor and a memory, the memory having executable code stored thereon for execution by the processor for performing the steps of obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the mutant or wildtype tumors; deriving from said differentially expressed genes a set of genes to predict p53 mutational status; and using the set of genes to predict disease outcome in the patient.

A suitable computer system may be a general purpose computer such as a PC or a Macintosh, for example. Computer software products of the invention typically include a computer readable medium having computer-executable instructions for performing the logic steps of the method of the invention. Suitable computer readable media include floppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes etc. The computer executable instructions may be written in a suitable computer language or a combination of several languages. Basic computational biology methods are described in, e.g. Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2^(nd) Ed., 2001).

Additionally, the present invention may have preferred embodiments that include methods for providing genetic information over networks such as the Internet.

Additionally, some embodiments of the present invention may provide a plurality of pharmaceutical targets for designing chemotherapeutic drugs for a variety of cancers. For example, the 32 genes most correlated with p53 mutational status could serve as potential molecular targets for chemotherapy. Chemotherapy drugs (cytotoxics) and antihormonal treatments are commonly used to treat cancers. In several patients however, treatment regimens involving cytotoxics and antihormonals have been known to cause mild to severe side effects. In breast cancer for example, these side effects include vomiting, nausea, alopecia and fatigue. The future of effective treatment for cancer thus resides with drugs that are more specific for their targets. According to some studies, about 68% of breast cancer drugs in the clinical developmental pipeline are of the targeted class. Therefore, molecular signatures such as those embodied in certain aspects of the present invention will provide important leads or will prove to be targets in their own right for targeted chemotherapeutic drugs.

In conclusion, the disclosed embodiments of the present invention define a gene expression signature a gene expression signature that can predict p53 status and survival in human breast tumours (the p53 signature or classifier). In independent datasets of both breast and liver cancers, and regardless of other clinical features, subsets of the p53 signature can predict p53 status with significant accuracy. As a predictor of disease-specific survival (DSS), the signature significantly outperformed p53 mutation status alone in a large patient cohort with heterogeneous treatment. The p53 signature could significantly distinguish patients having more or less benefit from systemic adjuvant therapies and loco-regional radiotherapy. Though the p53 pathway may be compromised at some level in most human cancers, analysis of transcripts involved in the p53 pathway suggests that the p53 expression signature defines an operational configuration of this pathway in breast tumors (more so than p53 mutation status alone) that impacts patient survival, and therapeutic response. In cancer, it is clear that not all p53 mutations have equal effects: some simply confer loss of function, while others have a dominant negative effect (such as trans-dominant suppression of wildtype p53 or oncogenic gain of function), while still others show only a partial loss of function where, for example, only a small subset of p53 downstream transcriptional target genes are dysregulated. For these reasons, no single molecular assessment of p53 status appears to provide an absolute indication of the complete p53 function. The embodiments disclosed herein suggest that by looking at the downstream indicators of p53 function, the functional status of p53 may be ascertained more precisely than using sequencing or biochemical means.

It is to be understood that the above description in intended to be illustrative and not restrictive. Many variations of the invention will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. All cited references, including patent and non-patent literature, are incorporated herewith by reference in their entireties for all purposes. 

1-44. (canceled)
 45. A method for predicting disease outcome in an early-stage, locally-treated breast cancer patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the tumor samples that may be mutant or wild type for the p53 gene; deriving from said differentially expressed genes a set of sequences to predict p53 mutational status; and using the set of sequences to predict disease outcome wherein the sequences consist of SEQ ID NO: 23, SEQ ID NO: 22, SEQ ID NO: 31, SEQ ID NO: 14, SEQ ID NO: 11, SEQ ID NO: 26, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 1, SEQ ID NO: 29, SEQ ID NO: 20, SEQ ID NO: 24, SEQ ID NO: 10, SEQ ID NO: 32, SEQ ID NO: 28, SEQ ID NO: 5, SEQ ID NO: 16, SEQ ID NO: 25, SEQ ID NO: 15, SEQ ID NO: 7, and SEQ ID NO:
 3. 46. The method of claim 45 wherein disease outcome is selected from the group consisting of disease-specific survival, disease-free survival, tumor recurrence and therapeutic response.
 47. The method of claim 46 wherein the disease outcome is disease-specific survival.
 48. The method of claim 45 wherein predicted p53 mutational status is obtained by ranking the differentially expressed genes according to their association with p53 mutational status, ER status and histologic grade of the tumor.
 49. The method of claim 48 wherein the genes are ranked according to a multivariate ranking procedure.
 50. The method of claim 49 wherein the multivariate ranking procedure is Linear Model-Fit.
 51. The method of claim 48 wherein predicted p53 mutational status is obtained by employing a supervised learning method.
 52. The method of claim 51 wherein the supervised learning method is Diagonal Linear Discriminant Analysis.
 53. A method of identifying a group of sequences for predicting disease outcome in an early-stage, locally-treated breast cancer patient, the method comprising the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wildtype for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the tumor samples that may be mutant or wild type for the p53 gene; ranking the differentially expressed genes according to their ability to predict p53 mutational status; employing a supervised learning method to distinguish between mutant and wildtype p53 gene expression profiles; obtaining a p53 classifier including a set of sequences capable of predicting p53 mutational status; validating the p53 classifier in independent datasets; and assessing the ability of the p53 classifier to predict disease outcome in the patient, wherein the p53 classifier includes sequences consisting of SEQ ID NO: 23, SEQ ID NO: 22, SEQ ID NO: 31, SEQ ID NO: 14, SEQ ID NO: 11, SEQ ID NO: 26, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 1, SEQ ID NO: 29, SEQ ID NO: 20, SEQ ID NO: 24, SEQ ID NO: 10, SEQ ID NO: 32, SEQ ID NO: 28, SEQ ID NO: 5, SEQ ID NO: 16, SEQ ID NO: 25, SEQ ID NO: 15, SEQ ID NO: 7, and SEQ ID NO:
 3. 54. The method of claim 53 wherein the differentially expressed genes are ranked by a multivariate ranking procedure according to their association with p53 status, ER status and histologic grade of the tumor.
 55. The method of claim 54 wherein the multivariate ranking procedure is a Linear Model-Fit.
 56. The method of claim 53 wherein the supervised learning method is a Diagonal Linear Discriminant Analysis.
 57. The method of claim 53 wherein disease outcome is selected from the group consisting of disease-specific survival, disease-free survival, tumor recurrence and therapeutic response.
 58. The method of claim 57 wherein the disease outcome is disease-specific survival.
 59. A computer system for predicting disease outcome in an early-stage, locally-treated breast cancer patient, the computer system comprising: a computer having a processor and a memory, the memory having executable code stored thereon for execution by the processor for performing the steps of: obtaining gene expression profiles from a plurality of genes from tumor samples, wherein said tumor samples may be mutant or wild type for the p53 gene; comparing said gene expression profiles to determine which genes are differentially expressed in the tumor samples that may be mutant or wild type for the p53 gene; deriving from said differentially expressed genes a set of sequences to predict p53 mutational status; and using the set of sequences to predict disease outcome in the patient, wherein the set includes sequences consisting of SEQ ID NO: 23, SEQ ID NO: 22, SEQ ID NO: 31, SEQ ID NO: 14, SEQ ID NO: 11, SEQ ID NO: 26, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 1, SEQ ID NO: 29, SEQ ID NO: 20, SEQ ID NO: 24, SEQ ID NO: 10, SEQ ID NO: 32, SEQ ID NO: 28, SEQ ID NO: 5, SEQ ID NO: 16, SEQ ID NO: 25, SEQ ID NO: 15, SEQ ID NO: 7, and SEQ ID NO:
 3. 60. A method for predicting disease outcome in an early-stage, locally-treated breast cancer patient, the method comprising the steps of determining if early-stage breast cancer tissue of a patient expresses a set of nucleotide sequences; and predicting the outcome of early-stage breast cancer based on the determination wherein the sequences consist of SEQ ID NO: 23, SEQ ID NO: 22, SEQ ID NO: 31, SEQ ID NO: 14, SEQ ID NO: 11, SEQ ID NO: 26, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 1, SEQ ID NO: 29, SEQ ID NO: 20, SEQ ID NO: 24, SEQ ID NO: 10, SEQ ID NO: 32, SEQ ID NO: 28, SEQ ID NO: 5, SEQ ID NO: 16, SEQ ID NO: 25, SEQ ID NO: 15, SEQ ID NO: 7, and SEQ ID NO:
 3. 61. The method of claim 60, wherein the disease outcome is disease-specific survival.
 62. The method of claim 60, wherein the set of sequences is immobilized on a solid support.
 63. The method of claim 62, wherein the solid support is a microarray. 